{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST For ML Beginners: The Bayesian Way\n",
    "**(c) 2017 Sreekumar Thaithara Balan, Fergus Simpson and Richard Mason, Alpha-I**. \n",
    "\n",
    "Download this notebook at: [https://github.com/alpha-i/blog/blob/master/notebooks/20170609_MNIST_For_ML_Beginners_The_Bayesian_Way.ipynb]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This tutorial is intended for readers who are interested in applying Bayesian methods to machine learning. Our intention is to teach you how to train your first Bayesian neural network, and provide a Bayesian companion to the well known [getting started example](https://www.tensorflow.org/get_started/mnist/beginners) in TensorFlow.\n",
    "\n",
    "So why do we need Bayesian neural networks? Traditionally neural networks are trained to produce a point estimate of some variable of interest. For example, we might train a neural network to produce a prediction of a stock price at a future point in time using historical data. The limitation of a single point estimate is that it does not provide us with any measure of the uncertainty in this prediction. If the network predicts that the stock will increase in value with 95% confidence then we probably have an easy decision to buy, but what if the network has only a 50% confidence? With point estimates we just don't know how uncertain we are. By contrast, Bayesian neural networks enable us to estimate the uncertainty in our predictions using Bayes' rule. \n",
    "![Stock price prediction'](./stock_price.png)\n",
    "Figure shows a point estimate (red circle) of a stock price 40 days in the future along with 95% confidence bounds (green dotted lines). Without a measure of the uncertainty we cannot understand how much risk we are taking when we trade."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About this tutorial\n",
    "\n",
    "In this tutorial, we will learn about:\n",
    "\n",
    "+ How Bayesian statistics are related to machine learning.\n",
    "+ How to construct a Bayesian model for the classification of MNIST images.\n",
    "+ How Bayesian neural networks can quantify uncertainties in predictions.\n",
    "\n",
    "For more background information on Bayesian Neural Networks, Thomas Wiecki's blog on [Bayesian Deep Learning](http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/) and Yarin Gal's blog [What my deep model doesn't know...](http://mlg.eng.cam.ac.uk/yarin/blog_3d801aa532c1ce.html) are extremely useful starting points.\n",
    "\n",
    "The tutorial requires [TensorFlow](https://www.tensorflow.org/) *version 1.1.0* and [Edward](http://edwardlib.org/) *version 1.3.1*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Neural Networks\n",
    "\n",
    "To understand Bayesian Neural Networks, we'll begin with a brief outline of Bayesian statistics. At its core, Bayesian statistics is about how we should alter our beliefs in light of new information.\n",
    "\n",
    "### Bayes' rule: \n",
    "\n",
    "Suppose that we have two events $a, b$ and we want to know the conditional probability distribution of $a$ given $b$, which is written as $P(a \\;|\\;b)$. Bayes' rule tells us that\n",
    "\n",
    "$$ P(a \\;|\\;b) = \\frac{P(b\\;|\\;a)P(a)}{P(b)}$$\n",
    "\n",
    "where $P(b\\;|\\;a)$ is the likelihood of observing event $b$ given $a$, $P(a)$ is our prior belief about $a$, and $P(b)$ is the probability of event $b$. Note that our prior belief about the variable $a$ is a probability distribution and that we obtain an entire distribution on the possible values of $a$ given $b$ as opposed to a single point estimate.\n",
    "\n",
    "### Neural Networks\n",
    "\n",
    "So how does Bayes' rule connect to neural networks? In this section we'll explain how 'conventional' neural networks already make use of Bayes' rule under certain assumptions on the regularisation term used during training. \n",
    "\n",
    "Suppose that we are given a data set $D= \\{(x_{i},y_{i})\\}_{i=1}^{N}$ consisting of pairs of inputs $x_{i}$ and corresponding outputs $y_{i}$ for $i=1,2,\\ldots,N$. We can use a neural network to model the likelihood function $P(y\\;|\\;x;\\omega)$, where $\\omega$ is the set of tunable parameters of the model i.e., the weights and biases of the network. For example, for a classification problem we could use a standard feedforward network, $y_{i} = f(x_{i};\\omega)$ followed by a softmax layer to normalise output so that it represents a valid probability mass function $P(y_{i}\\;|\\;x_{i};\\omega)$.\n",
    "\n",
    "Traditional approaches to training neural networks typically produce a point estimate by optimising the weights and biases to minimize a loss function, such as a cross-entropy loss in the case of a classification problem. From the probabilistic viewpoint, this is equivalent to maximising the log likelihood of the observed data $P(D\\;|\\;\\omega)$ to find the maximum likelihood estimate (MLE), following [Blundell et. al. 2015](https://arxiv.org/abs/1505.05424) \n",
    "\n",
    "$$ \\omega^{\\text{MLE}} = \\text{arg}\\underset{\\omega}{\\text{max}} \\;\\log{P(D\\;|\\;\\omega)}$$\n",
    "$$ \\quad\\quad\\quad\\quad = \\text{arg}\\underset{\\omega}{\\text{max}} \\;\\sum_{i=1}^{N}\\log{P(y_{i}\\mid x_{i},\\omega)}.$$\n",
    "\n",
    "\n",
    "This optimisation is typically carried out using some form of gradient descent (e.g., backpropagation), and then with the weights and biases fixed we can predict a new output $y^{*}=f(x^{*};\\omega)$ for a given input $x^{*}$.\n",
    "\n",
    "Training a neural network in this way is well known to be prone to overfitting and so often we introduce regularisation term such as an $L_{2}$ norm of the weights. One  can show that placing $L_{2}$ regularization of the weights is equivalent to placing a normal Gaussian prior $P(\\omega)\\sim\\mathcal(0,I)$ on the weights and maximising the posterior estimate $p(\\omega\\;|\\;D)$. This gives us the Maximum a-Posteriori estimate (MAP) of the parameters (see chapter 41 of MacKay's [book](http://www.inference.phy.cam.ac.uk/itprnn/book.html) for details):\n",
    "\n",
    "$$ \\omega^{\\text{MAP}} = \\text{arg}\\underset{\\omega}{\\text{max}}\\;\\log{P(\\omega\\;|\\;D)}$$\n",
    "$$ \\quad\\quad\\quad\\quad\\quad\\quad\\;\\; = \\text{arg}\\underset{\\omega}{\\text{max}}\\;\\log{P(D\\;|\\;\\omega)} + \\log{P(\\omega)}.$$\n",
    "\n",
    "From this we can see that traditional approaches to neural network training and regularisation can be placed within the framework of performing inference using Bayes' rule. Bayesian Neural Networks go one step further by trying to approximate the entire posterior distribution $P(\\omega \\;|\\; D)$ using either Monte Carlo or Variational Inference techniques. In the rest of the tutorial we will show you how to do this using Tensorflow and [Edward](http://edwardlib.org/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing data\n",
    "Let us import the [MNIST images](http://yann.lecun.com/exdb/mnist/) using the built in TensorFlow methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from edward.models import Categorical, Normal\n",
    "import edward as ed\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Use the TensorFlow method to download and/or load the data.\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling\n",
    "\n",
    "Recall that our task is to classify the handwritten MNIST digits into one of the classes {0,1,2,...,9} and give a measure of the uncertainty of our classification. Our machine learning model will be a simple soft-max regression, and for this we first need to choose a likelihood function to quantify the probability of the observed data given a set of parameters (weights and biases in our case). We will use a Categorical likelihood function (see Chapter 2, [Machine Learning: a Probabilistic Perspective](https://www.cs.ubc.ca/~murphyk/MLbook/) by Kevin Murphy for a detailed description of Categorical distribution, also called Multinoulli distribution.).\n",
    "\n",
    "We next set up some placeholder variables in TensorFlow. This follows the same procedure as you would for a standard neural network except that we use Edward to place priors on the weights and biases. In the code below, we place a normal Gaussian prior on the weights and biases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ed.set_seed(314159)\n",
    "N = 100   # number of images in a minibatch.\n",
    "D = 784   # number of features.\n",
    "K = 10    # number of classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create a placeholder to hold the data (in minibatches) in a TensorFlow graph.\n",
    "x = tf.placeholder(tf.float32, [None, D])\n",
    "# Normal(0,1) priors for the variables. Note that the syntax assumes TensorFlow 1.1.\n",
    "w = Normal(loc=tf.zeros([D, K]), scale=tf.ones([D, K]))\n",
    "b = Normal(loc=tf.zeros(K), scale=tf.ones(K))\n",
    "# Categorical likelihood for classication.\n",
    "y = Categorical(tf.matmul(x,w)+b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Variational Inference\n",
    "\n",
    "Up to this point we have defined the likelihood $P(y\\;|\\;x,;\\omega)$ and the prior $P(\\omega)$, next we want to use Bayes rule to compute the posterior $P(\\omega\\;|\\;y,x)$. However, we immediately face a problem because in practice the probability of the outputs $P(y)$ is computationally intractable to compute for large instances and so we don't attempt to calculate the posterior directly.\n",
    "\n",
    "To tackle this problem we will instead be using Variational Inference (VI). In Variational Inference we choose a family of parameterised distributions $Q(\\omega;λ)$ over parameters $\\omega$ to approximate the true posterior, and then optimize the parameters $\\lambda$ so as to match the true posterior distribution as best as possible. The core idea is to minimise what is known as the Kullback-Leibler divergence between the true posterior $P(\\omega\\;|\\;y,x)$ and the approximating ditribution $Q(\\omega;λ)$, which can be thought of as a measure of the disimilarity between two probability distributions.\n",
    "\n",
    "The theory behind VI is beyond the scope of this blog, so more more information a quick introduction to VI can be found in Edward's documentation and a detailed one in Variational Inference: A Review for Statisticians by Blei et al.. Chapter 33 or MacKay's book is also a very good reference.\n",
    "\n",
    "So next we use Edward to set up the approximating distributions $Q_{w}(\\omega)$ for the weights and $Q_{b}(\\omega)$ for the biases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Contruct the q(w) and q(b). in this case we assume Normal distributions.\n",
    "qw = Normal(loc=tf.Variable(tf.random_normal([D, K])),\n",
    "              scale=tf.nn.softplus(tf.Variable(tf.random_normal([D, K])))) \n",
    "qb = Normal(loc=tf.Variable(tf.random_normal([K])),\n",
    "              scale=tf.nn.softplus(tf.Variable(tf.random_normal([K]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We use a placeholder for the labels in anticipation of the traning data.\n",
    "y_ph = tf.placeholder(tf.int32, [N])\n",
    "# Define the VI inference technique, ie. minimise the KL divergence between q and p.\n",
    "inference = ed.KLqp({w: qw, b: qb}, data={y:y_ph})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialse the infernce variables\n",
    "inference.initialize(n_iter=5000, n_print=100, scale={y: float(mnist.train.num_examples) / N})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to perform Variational Inference. We load up a TensorFlow session and start the iterations. This may take a few minutes..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will use an interactive session.\n",
    "sess = tf.InteractiveSession()\n",
    "# Initialise all the vairables in the session.\n",
    "tf.global_variables_initializer().run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000/5000 [100%] ██████████████████████████████ Elapsed: 5s | Loss: 27902.377\n"
     ]
    }
   ],
   "source": [
    "# Let the training begin. We load the data in minibatches and update the VI infernce using each new batch.\n",
    "for _ in range(inference.n_iter):\n",
    "    X_batch, Y_batch = mnist.train.next_batch(N)\n",
    "    # TensorFlow method gives the label data in a one hot vetor format. We convert that into a single label.\n",
    "    Y_batch = np.argmax(Y_batch,axis=1)\n",
    "    info_dict = inference.update(feed_dict={x: X_batch, y_ph: Y_batch})\n",
    "    inference.print_progress(info_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating Our Model\n",
    "We now have everything that we need to run our model on the test data, let's see how good our model is! The major difference in Bayesian model evaluation is that there is no single value for the weights and biases that we should use to evaluate the model. Instead we should use the distribution of weights and biases in our model so that the uncertainties in these parameters are reflected in the final prediction. Thus instead of a single prediction we get a set of predictions and their accuracies.\n",
    "\n",
    "We draw a 100 samples from the posterior distribution and see how we perform on each of these samples. *Taking samples be might a slow process, may take few seconds!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the test images.\n",
    "X_test = mnist.test.images\n",
    "# TensorFlow method gives the label data in a one hot vetor format. We convert that into a single label.\n",
    "Y_test = np.argmax(mnist.test.labels,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Generate samples the posterior and store them.\n",
    "n_samples = 100\n",
    "prob_lst = []\n",
    "samples = []\n",
    "w_samples = []\n",
    "b_samples = []\n",
    "for _ in range(n_samples):\n",
    "    w_samp = qw.sample()\n",
    "    b_samp = qb.sample()\n",
    "    w_samples.append(w_samp)\n",
    "    b_samples.append(b_samp)\n",
    "    # Also compue the probabiliy of each class for each (w,b) sample.\n",
    "    prob = tf.nn.softmax(tf.matmul( X_test,w_samp ) + b_samp)\n",
    "    prob_lst.append(prob.eval())\n",
    "    sample = tf.concat([tf.reshape(w_samp,[-1]),b_samp],0)\n",
    "    samples.append(sample.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5f90125eb8>"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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jVatW5bpMiRIltHDhQi1cuPDmVAoAAPLkUqhv2bLF6edDhw4pJiYm7xXbbLLZ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fa4d874e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the accuracy of the model. \n",
    "# For each sample we compute the predicted class and compare with the test labels.\n",
    "# Predicted class is defined as the one which as maximum proability.\n",
    "# We perform this test for each (w,b) in the posterior giving us a set of accuracies\n",
    "# Finally we make a histogram of accuracies for the test data.\n",
    "accy_test = []\n",
    "for prob in prob_lst:\n",
    "    y_trn_prd = np.argmax(prob,axis=1).astype(np.float32)\n",
    "    acc = (y_trn_prd == Y_test).mean()*100\n",
    "    accy_test.append(acc)\n",
    "\n",
    "plt.hist(accy_test)\n",
    "plt.title(\"Histogram of prediction accuracies in the MNIST test data\")\n",
    "plt.xlabel(\"Accuracy\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a range of accuacies for the samples. Note that posterior distributions of weights and biases refect the information gained from the entire MNIST test data. Thus the above histogram is representative of the uncertainty coming from the statistically possible range of weights and biases.\n",
    "\n",
    "We can perform a model averaging and try to get a equivalent of a classical machine learning model. We do this by stacking up the predictions of the 100 samples we took from the posterior distribution and then computing the average of the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy in predicting the test data =  92.44\n"
     ]
    }
   ],
   "source": [
    "# Here we compute the mean of probabilties for each class for all the (w,b) samples.\n",
    "# We then use the class with maximum of the mean proabilities as the prediction. \n",
    "# In other words, we have used (w,b) samples to construct a set of models and\n",
    "# used their combined outputs to make the predcitions.\n",
    "Y_pred = np.argmax(np.mean(prob_lst,axis=0),axis=1)\n",
    "print(\"accuracy in predicting the test data = \", (Y_pred == Y_test).mean()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should also look at the posterior distribution. Unfortunately, the number of dimensions is quite large even for a small problem like this and so visualising them is tricky! We look at the first 5 dimensions and produce a triangle plot of the correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5f88242f60>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IotkjI6obRusGk5XiMIiQ0RNdM1o0W+2C2qDYISKc8OnNZLZhVlGWIL2T+24E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\n4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2D73kbRx4j0qMXssizee+GNPQ3YV93G2/PruyGrVq3A6nn5WDwjB2k6NUxmO46cbscB\nnqmc1fVdI0YcaVQiMfhfR406PA+KCp/5Ylq1HB6GQU+/Laj1lFwOnW4PaeRbDDY5idS/a8YT9ppo\n1cLjN/6zInyvpVIhQ7KW+1l02cxcPP/QEqxbODFg48vXp2c74XAFjuW5FdemrlvsLrz6/tnh2QsH\nasTVtfGC9wodPXqU9039/f0RKUw8WL78ehQXl2Dfvj3YsuWfYDBwzwP2V93k7VGdW5rP+5quQQe+\n6BzEpHQtSgQSQPTYvHN/83T8jTVfE1NUMNnd6LG7YXZ5kDoU3CtLM3C0pQ/Hr/RjfqF3NK9qZgl+\n+/dj+ORMMzavHrmp9/LlK/CrX/1/9u48oK0y3R/492QPJECA0LKXpUBboBvd94pdtNq6VjvjMpuz\neOeO1+nPuaOO46xene3O7h11dEZHra1Oq7VqF2o3qKWlLYVSlpZ9KUsSIGRffn+koYTsEJITeD5/\n3Ds2h+QhPDk5z3nf93l/jy++KMW992736bUJuxlNZigG9MhJZW9Lf7uk+But/anxSFjy1mLZ10Ya\nnkYtRAIuFuTIUTaipbQnAj6DCAEPqiEjGNjGJiqv9uHNg7UoXpiC2KibU9L1RjNKL3W6fa51C5Jx\nz5osKPq1MJos4PM4OHqhw6FA89REYnTXMhqVmBxG/h3f/KwWpT7m5mjMjf8TKxUhQsRDa7d6+DGd\nwYL9JxsxNKRHnJutIkQCLsQCLpRepqPpDRb845MafG3L7LAchSCB5ctG2btKGhzy0S5FHomv3JYH\nLoeDaInQYzdSV9Qe1rmJBFysKLBNXbfHeLKyw+GGhbtZEOHYIRLwULB95zvfQV5eHng850M6Ojwv\n9p7MOBwO7rrrPvzudy/hk0/24+GHv+rTz51raAeXw6DAQ3fI0y22QnjFDPdTJgFAa7JlocTHBfUM\nwyApkg+VwYw+rWm4YIuL4CMlWohGhRYGswUCLgciPg8FMxJxpq4VvQNDiI+62Z0yOjoaWVnZuHLl\nMgwGA7X4nwR6VDpYwf7pkACQdGOEraOXCrZw5K3FsnJA7/Mc/dEb9sZIhMhLl2HHrTPB5XBQ62F6\nzkgWC6C1b6p949/6BvQ4WtGOoxXtiIsSYsXcZNyxLA3tPYMeRy9W5E9Dv1qPo1ezR5gAACAASURB\nVOfbUXm1D30Dep8Wzdu561rmqWU6CS+1Lcox/6wVwP/bPg8pCRL89I1yl8dUXlVgTmYsjl9wvrEg\njxEjI0nq8rHRTl/uhiRCEHYbC5PA87bxtKcbcR29Q3j7UB123JoDIZ+LwqxYHL84thsWdjGRAnz3\nngIkySXDN7DePlzn17Rjd+dafze3Dza3348/+tGPcPnyZTz77LNOjz300EMTGhTbrV69Fn/5y+9x\n8uQxnwq23oEhNHT2YW5GIiKErkfFTBYrzrUPQMznoGC65/U59osAixU+XxBE3iju9KPm8qRGi9DW\nr0fvkBFJN6YZZU6PxZm6VrT19jsUbACQk5OH+vo6tLQ0Izt7pm8vTlire7ilP/svCGOjRRDwOVSw\nhSnPG0XbuiaarRafvii9jT75um7IZLbCZHa/5qdvQI8PT1zD+dpuDGo83xn+5VsVsIyq53xZNG8X\njl3LiO/G26yGAZCSIIFWb3L7PH0DOlQ2uN4rtrVbjfYe51EQd8JxY+GpaCKLDF82nvaU1xYrcPR8\nBxgOAwbAmRr326j4akBjAJ/HcVg/50sTqpEKR01792UUkQ3cFmx33303uFwuhoaGEBnpeNG+dOnS\nMb/gL3/5S1y8eBEMw+Dpp59GYWHhmJ8rVEQiMQoK5uHs2S+gUqmGN9V252R1EwBgWV6622OqutQY\n1JuxKiMGfK7nBBHzOIDejEGjGTI3GwaOZrxxUcIdtS4u8kZHKd2IucKxUtvFe/+Qzul5kpKSAQDd\n3depYJsErts7RMawf4SNwzBIiotEW48aZouFVSdS4p2nqYxDOiP+8zdHESv174vS3ejTyBG4vgHn\n85i/XE33GW10seYP+xokyuvJy9MNC19YYRvt2HHrTI/P42nKmT83EMJ12thUEYwiw5eNp33J69JL\nXQHbxkLA5+L3eyqHf+e8NJnfn6nihY69JLyNIrKFx7/q1q1bnYo1AHj88ccBAK+88opfL3bmzBk0\nNzdj165d+MUvfoFf/OIXfv08m2Rl2S4IWlqaPB5ntVpx+EI9uBwGK+fMcHvM59eUYAAsT/M8HRIA\n4kW2IqtNbfQ53m6t7djoUS1/9fbNtUcM1dk/7CYXVyBSqW2t2+Dg1F3HOJl0h0mHSLvk+EiYzNbh\nuEl4Gd2AQWS/YWSwwGodWxMSV+wjcD//xhI8+8hCv6YmhoLOYEHJufZx/96E3XLTxtdd27ZRcKOH\nvbACJxw3Fp5KAtHAyRtfNp72vDebTSD3HNQZzA6/86mqLoe2/d7ERYmc1iZ7GkVkU3OScZXhJ06c\n8Ov4srIyFBcXAwCysrLQ398Ptdr3IXo2sW9r0N/f7/G46pbraOpWYmleOmIiXV8UV18fQlu/HgXT\nJZC72X1+pGgBF3KJAAq9GS2DhuHOj+50aYzo0Jgg5jKIEzkWbNdvdO2JH9HAZEhnu1sR6Wb6Jpk8\n7HuwTQuTu6j2dv7tPTQtMhyNLKSe/+piRAhdT+EJ1BelkM9FZmJ0yLeB4DAAc6NA9bSZN9suEMj4\nmS0WvH24Ds++chplVV0QCbgQ8MZ+6VVR24NtqzIdbnzE+HDd4C+aostewSoyfN14evv6bKxbkDzh\nN8YEbs+dvr/w6Lz2ZRSRLcZVsHkrFEbr7e112L8tNjYWPT3+zT1lC/vvzvGSoe+XVgEAti6Z7fJx\nvcmCj2p6wWGAjTlxPr02wzBYmBINAYdB46ABVQod1Eazw9/DarVi0GjGFaUOtSo9eAwwK1YEzogp\nkTqTBdcUWiRKBYgYkcAdClub/2kyqdNrq9W2x+wjbSS8dSm0iJEI/N5sNVSS4qnxyGQg5NsuWpVu\n2jwH+ovymYcXIDUhdEXbmnlJeOGxpfjdd1fimYeL3F5esO0CgYzf6JEQncEMg8kC4RiLNvtGwTuK\nc/Dco0X4/vZ5eOahhQGbJRErFdL2ESwXzCLDl+1FuBwOHtqQizXz3HdBH68YiQAGN1129QYzls5O\nuLl1hpvCLjVB4pTXvowissWYNs62c7dPmK98KfhksgjweMG/mJTLnYuVkUwm27qIlJRpbo+93Hwd\n5XWtmJuVhDULXZ/83ihrQZ/GiM1zEpCf6VvBZlecK0d5qwo9agMUPVqIeBxE3LjwHjKYob+xLi1G\nxMOitBhEj9qz7VBNN0wWK5Zlxzn8Dle7+iDkczEvNxkCvmOKKJW2Ajs3N8PrewR4fx8nm1Dlqyve\n3nuD0QzFoA5zMuOC9nca7+sU3Hhvewb1AYl5quXnaKHMV2m0GHKZ2OX01vgYMbJmxHndG8gfv/2v\nNfj2i0fQq/K8pi19mgRagxm9Ki3iokVQDOhgHuP6tASZGEvzE/HVO+aAe2Ntcny8JCC/91TOXTb9\n7p5i0RlMqLzqugkIh8sBfNhnypXPL3ZALOTjdFUnelRaxEeLIAzAZ2V9USq+fU9hQD53bPobhZK7\nc+x43p+JOne6i+l7Dy6EzmCCckAPWZTQ7XN/78GFEIsF+PR005jX9ErEPJft/JcXJuFszXWXv7MV\nwNWOASzJn46NS9Px89fPoMfFcXqjGTGySKf4V8xNxocnrjkdv2JuElKSXPeoCEV+B+7b0AcJCQno\n7e0d/u/u7m7I5Z7nviqVwd8kVy6Xoqdn0OMxDQ2NAACRKNrtsX943zZldPuKQpfHlLcN4GhtL6ZL\nBVid6v01R8c41K/BLCkfCXwOurUmDBjMUGhs+wkJuQzkIh4SxDzEibgwqHXoUd+8UNEazdh3oRN8\nDoOCONHwa/cNalDf3ovCjET0q5wT/uLFS+DxeJBI4r3G68v7GEhs+IIIRb664st7396jhtUKxEqE\nQfk7BSQfrFaIBFxca1ON+7mCnZ/uYgilUOdrYVacyyYkhVlxGOzXYuRfZ7zd0LqVGvR5KdYA294/\nzz1aBLXWiI9ONaHHh58ZLUUeiW9vy0dslAhCPhcKheOIsD+/tyuhyt1Q56tdqD+3dt7+Dm3dg27X\n2+oNJizPn47aFhUUAzpESwSIFPOh1ZmgHNTD0+3sYxVtMJhuHmHPUS4H4PO4MBjNkElFKMyOg15v\nQmn1dafnSIyNgMFkhnJQD5lUhPk58di+Psun/POGDedWu1DnrKtzbCDen/GeQ8YSEw/w+tyrC6bj\nk9ImP1/9JrXWdGNNGgOD0Yz4GDFmJkfjtiVpMBhMbrv/9qh0OFDaBNWADr1uPnO9Ki2uNvU5NdK5\nY1kaNFrD8DYx9s/DHcvSht+Tkd8/KUkxE5bfnvLVbcFWXV2NOXPmBDSQFStW4I9//CMeeOABVFdX\nIyEhARJJaNcWjNW1aw2IiIhEQsI0l49fbOzA+WsdmJ+ZhMIM573XGhVavH+pG2IeB48sTPTaGdId\nhmEQL+YhXmz7U1qtVp9GPvfX9GJQb8amnDhIR3SaPFltK0SX5aY5/YxarUZDQx3y8mZBKGTPMDEZ\nm+vDLf3Do+EIYMv3FLkE1zoGYDSZwWfJaCYZm9H7qd28cLw5I8HfbmjuCjtJhABCAcfjXmqArTW6\nWmvE0fPtOH3Z+ULXLloigMViwaDG+W6wVm8eLtbG+nuT8DUyZ92RSUV4aGMuADi0KO9RaWEwmvDH\nD6rQ76bj48hizfF1AbPBjOX50/HQxlzwuAzePVIPkYA73PhBJOBgeUEiHrxlJkxmK6v3nSLusekc\nYj/nioU8GIxmt10jY6VCPH5XPgY1Rvzvnkq3z2c/RyfGRsBksaC0qgtXWpTITY3B2vlJuHRjn0tX\nrjQrIZMKoHAx3T5GInQ5xdHTNjGuvn/se3MGu6Ov24LtySefhFqtxvLly7Fq1SqsXLkSsbGxDsfM\nmDHDrxdbsGAB5syZgwceeAAMw+DHP/7xmIIOtaEhNVpbWzB37nyXxZHVasU/jpwDADx8y0Knx3uH\nDHj9bAcsViu+vCAR8sjALRj2pVg709qPL1oHkCgVYG3mzeFeq9WKg+frwOUwWJWf4fxzZ8pgsVhQ\nVLQkYPGS0LmusN35mx4mDUfsUhIkaGjvR0evBunT2XHHn4zNyC9KroAPs8HodOHoa8tlb4Xd3hPX\nvBZrdgfLW1F1zfVUNjt3F9OA95bo3vaRI+FtdM66MrL5QVy0yCl3pWK+xxzzpLZFNRzHkXPtDo/p\nDBbUt9qapdGm7OGLDecQ+zm3orYbikEDOIxt6wh3XRsX5MqRkRQNvdGMOB+2uOhU3Byd7BvQo7T6\nOkQCDuZmy9Hn5maaSq2/0UTN+bMTKeZ7fI9cfR5cff98eOIaNFpD0Fv+uy3YPvvsM3R2dqKsrAzH\njx/Hr371K8jlcqxatQqrVq1CUVERfvrTn/r9gjt37hxXwGxQW3sFAJCbO8vl42cb2lDX3ovls9Ix\nMyne4TGN0YzXyjugMVpwX0ECcuXO2yZMpPpeDd6v6oGYx8HDCxPBGzGyd6m5C83dKqycPcNlR8uS\nkkMAgJUr1wQtXjJxum6cDBPCYNPskezNI1q71VSwTRJCPhfy+EiHaSZ6oxk9Ki0qal1vtjp6Y19P\nhd09a7L82ly1sqEPynEs3Pd1sTpdME8+njr4AbZRhgW5coeREFe5C+jdrufxRjGoQ22z0m0crd1q\nvH2oDg9tzPP7uQm7hPIcMjpv7fv82W+MiQQ3p+eOHP0T8rkozIrD0fMdfr+mzmDBF5evO4wajxQj\nEUJvct0lU6MzQm80+1zY+rJxeDCLZI9r2BITE3H33Xfj7rvvBgAcO3YMr776Kv72t7+hpqYmKAGy\nUW2t7XefNct158fdJ2xDvQ+umefw7xarFe9cuI6eISPWZsqwxIc91wKpWanF62dtH5CHFzqP7H14\n+jIA4E4XHS2vX+9CRcVZzJo1B6mpztMlSfi5rtSCQXhsmj3SyIKNTD6j79q6M3IUy9sX6+q5SW67\nqrmiGtIjRiLwuAmxJ9QSfery1MGPYYAn7p+LlBHbTHjKXT6XixWF8ai+pvAvF63wOOUMAM7X9+L+\n9b5fvBIykrcbEwAQIeTh6YcWQh4jdppiaG/GwwAe12u6465pYV66DGVVXS4fU97osOprgetLN85g\nFsseCzaFQoGysjKcOnUK586dQ0JCApYsWYLvfe97wYqPlewjbDk5ziNsDZ29uNzajYXZyciY5jiF\n9FSTCjXdQ8iJj8Btef51hByvjgE9XjnTAaPZiocXJmJmvGOStff144vaFuQkx2NWaoLTz3/22QFY\nrVZs2nR7sEImE+y6QoO4aBH449gTKBSSb7T2b+1mx6J2EljvHKlHyahpXK6MHMXy9sUKq9XtugpX\nYqUiFGbF+n0HWCYRYmHezdGT8TZKIeHH3ibc9RoeEeSjbpB5zF21HtXXFJg/Mx6r5yXhT+9f8imH\nfbkA7lcb0KPSQsDjUH4Sv3nKWzuVWg8Bj+OQW6NH5cZSrAGAflSLfwa25RIP3JKF2haly8+Jv236\nPX2WQ9Hy323Bduedd0Kj0eD222/Hli1b8Nxzz0EkErk7fEqpq7uC+Hi505o+ADh0vh4AsGWRYzHX\nrzPhQG0fIvgcPDhvmsN+aBNNoTHib1+0Q2ey4EvzpqNgunOjl/1namAFcNeyfKd1cCaTCZ9++jEi\nIyOxevXa4ARNJpRGZ0L/kAFzMpxzmO3EQh4SYsRo6xnyuckOCQ96oxmllzp9OnbkKFa0ROh2oblM\nKoJcFoH5OXKv64pGPvf29dngcjmovNrnttPfSDESAZ7/6iJIIwTDmyX72iiFTB72zYZd5ZqrkVdP\nF4UAoFIbcPR8B7hcjl857I2Az8Fv3j2P/iEj4ig/iZ+85S3gXNR4GpUTCTiIFPGHu5ZGiHh+zaKx\nwjbr5t/HG5GXJsMpF6Ns/s588PezPNHcFmzbt29HWVkZPvnkEzQ1NaGlpQXLli1Denp6MONjHZVK\nCaVSgSVLljs9ZrFaUVbTjCixEAuykx0eO1yvgNFsxbbZcoeujBPNZLHizfOdUBvM2DpbjvnJzmt+\n9EYTSi5eRaxEjGV5zn/f06dPQalUYOvWuyEShdf0OeKaff1aYlx4rp9JnSbBuVrbxXBcNN1Imix6\nlBqvjUHiohzXQ9im2NRDNeR6ypj9i9VVV7W5M+PAALhQ3+fUac1ktqJ4YQoe3DgL3//9Ma9T0ory\nEiCNsE0z97VRCpmc/Ong5+micKTzdb147iuLUNuiQnuPeni90FjpjZbhUQp/8pNGjQkA8LgMIkR8\njwXb6KKmX613e7zOYMHTXy6EgM9FtEQIHpfBrpIGnLrUBa3e93Wcxy50jGh8wrhcQ+cPV5/lFXOT\ncMey4C8Ncls5fOlLX8KXvvQlWCwWVFVVobS0FM8//zx6e3uRn5+PF154IZhxskZbm+2klpbmXNi0\n9qigUGuxtiDT4S6V3mTB2bYBxIp5KEqJClqsgG0aZqtKjwXJUqyc4XrNXHldK4b0BmwuKnBoQmJ3\n5MhBAMCmTVsmNFYSPJ19tn2hkuKC2/QmUGZMl+JcbQ+augapYJtMvIyWPnFvAXLTYx3WQ/z0jbNu\n78SmJkiGv3DtBdgdy2dAqzdBLORBqzchWiLEvWuzhy9C7RcKN0fHRB6LNSGfg5WFiQ7TINm0UJ0E\nn78d/LatyoRWZ0J1k/u1aooBHd45VDemtbt8HoMIIR8DQwaPW1ucrOzEtlWZiHBxU9nf7TXI5Lar\npMFtLo4csbUzWyw48EWz2+fjMLZRO/tNL8BWLPH5XBw52wqD0bcOv6Mbn9i3uBjrOdfVZ3ki92Hz\nxOtQD4fDQUZGBrq6utDb2wuFQoGKiopgxMZKPT22VqKu9l9r6LQtosxLcVwD1tCngdFixfxkKbic\n4E3fMpktONKggJjPwdbZcrdTx8pqWwAAq/MznR5Tq9U4e/YMMjIyMWOGc6t/Ep46+8J7hG1Gou3G\nR1PXABbmykMcDQkUeYzYbfcvkYDrUKwBwNteLmCHtEbojRbsPXHzQjNGwodIwIfeaIJy0DB84blt\nVSb61Xp8Vt6KoxU319D1DbjfODs6UoCffm2xw0UG2xaqk9Dx1MFPbzRDMaDD4XNtqGzohWJAD5lU\nAAGfgcHoPHxmBXCmxnUr8+hIPvh8LnrdbPK+siAR96+fiR6lBr/ZdcFtwaYzmPHOoTp8bYtz4zEa\nNSZ2nm5KMQDmZMY6FfK7Shpw/IL76e4Wq+2Yr9yWN/xzbx+qG1MnyZHsW1yMFxs6+rot2L744guU\nlpaitLQUzc3NKCoqwooVK/DII48gNTU1mDGySn+/7Y8fHR3j9Nh1pa3iTo5zHEXruPHlPUMW3OmE\nNd0aaIwWrM2UIVLg+u6C1WrFxWudiJWIkTFN5vR4eflpmEwmrFq1doKjJcFkH2FLDOMRNgBo6qLG\nI5OJkM/FioLpTntHAcCKgukOxZreaMb5+l6Pz6cc1OOdQ3UO6xmUaiMA4/B/2y88T1Z2QGewwJ97\naotmJTgUawD7FqoTdhk5UjU6Rzx1RQXgdhrkoMaItQuno+Rsq9NjqQkS7LjVVlB9dqYV/UNGp2NG\nutKidGp9TqPGZCRPN6WsAI5f6ISAxx0u5H3pKAkApTfO0ztuzcH7x67i2IXxFWvA5LpJ5rZge+GF\nF7Bq1Srs3LkTCxYsAJ/PD2ZcrKXX25JULHYuvjR624lQInb8QtbdGMqVuCmaJkr1jTvPcxOdm4zY\n9fQPQTWkxYrZM1yOwJ09ewYAsHSp85o9Er46+zSIFPEgjQjPz3WkiI+EGDGaOgeo8cgk88AtM8Ew\njG1EbFCPWKnz9BrAdtHgbV1ZjESAKy1Kn17XPurgaW1QdCQfgxrjmNckUbt/4sum2iIBFyI+Byov\nxZWdTCrCY9vywYEVFbW2z01MpADzc+XYUTwTXA4Hbx+uc9mIYTTFgHPrcxo1JiP50nBkZCHvS0dJ\nu9KqLpTXXIfR7HmRpkwihFKtH96s2932AJPpJpnbgm3v3r3BjGNSsK//Mo7atE9wo226xuh6M7+J\nYLVaUd+rRaSAi+Ro98naeF0BAMia7rpb4KVLFxEVFY0ZM5ynS5LwZDJb0K3UIjMpKqwLnRmJUpyp\n6UZvv86pVTYJX76u/4mWCBHn5aJh1oxYt3vyjAWHw8HiWQm4ZUEKhAIuTGYrTGbnJgz+NJ0gU4fe\naHa7EfxIOoMZfK7v5+b5OfEQCWyXcwxju3jlcpnhJRi+jnAAgFDAhVjIQ7dSM5zTNGpMRvKlUc7I\nQt6XAm8kb8VaXJQQzz26yGEt8v6yJpysHH9nSDYLXrvCScK+tYFW69zmOS7KdofpukqN2Wk317gl\nSW0ns0aFDrny4ExB61Ib0K8zYV6ixOMWAs3dtimeaXLnKZ4KRR96erqxZMnysL6wJ46uK7WwWK2Y\nHqbr1+xmTI/CmZpuXO3op4JtEvK2ZsDbRUNKQiR23DoTV5oVXqea+Uo5qMfpy904fdl20c1hAD6f\nA4PB4tSEwV509ig1AMNAHiOm5gxTmNliwVuf1fqci4Na953x7KMJsVIhFuTacu7vH1W7XGNmtlgx\nf2a87xfLJgt+8voZh/Wd29dn06gxcbB9fTbMZstwV8bRYiTC4ULe106ovhrSGfFRaZMtBosFv363\nCu09jmuZY6UCLMhNcHuTLBy7nVLB5ieZzDYSpVA4r52YmRQPAKhuuY51hVnD/54jj4CQx8GZ1n6s\ny5JBGISNis+2DQAA8l3suTZSc7dtutCMac4jbHV1tQCAnJzcAEdHQsl+YrNvQB2uslNsXU8b2vqx\ndPb0EEdDQmHbqgycrOx02aREqzODy+EgLz12eG1EoFmsgN7gujW62WLB+8euBrSrnv0iQxpNNyjC\nza6SBp+mJPpCwOfAYLTACkCrM2FQY8TpKtcNHY6db3doouON2WIdLipH5jSNGpORTGYrNi5Og8UK\nl2vNNHoT3j92dfh8ZyuurDh2vn3cW1LoDJbhvKxtUblsPBUh4rtshhPO3U6pYPNTcrKt4Upzc5PT\nY9mJ8YiJFONkdSO+sXExhHzb2yvkcbByRjSONChx4Eov7spPcPrZQFJojDjV1I8oIRdzpnm+KG/o\n7EWkUICEGOfC7upV2ybg2dnUAWoyaeuxNRxJkXsu5tkufZoUfB4H9W39oQ6FhIhaY4TeRbEGAIpB\nHa619+PetZmoqOtxWdRNBPvajfePXfWrq56nO76jLzLkMjEKs+LC4iKD+D4lMTpSgH43+wkCtuUV\nBtPN/dOUg3qcqupCee11l50lAc9rMn1lz2l/tiogk9Poc5FMKkCyPBI9Si0MppvdR3UGs8P5jsvh\n4KENuYDVOu7Oj3bnrnS7/bx09A5hUGNwagoVzt1O6Uzvp7S0dAiFQlRVXXJ6jMflYOOCmVDrDNh3\n+rLDY8XZsUiQCHCquR+fX/NtEfxYaI1mvH62AyaLFbfnxYPvYl81u75BDdr7BpCbInc5bbK+vg4A\nkJ09c8LiJcFnH2FLCdL03InC53GQkRiFth61XxtrksnDvjbCFQbAr969gF/881xQp8wqB3XoUWo8\ndtXTj1jPbLZY8PbhOjz7ymn88P9O49lXTuPtw3XQ6I3oVmqgN5qHLzL6BvSwAuhWanH4bBt2lTQE\n6bciY6U3mnGtvd9j04VFeXI8uX0uvrVtjsfnEgldF0juirVAsa9HAm5OVaZibWoafS5SDBrQ3jPk\nUKyNdL6ux+F8t+PWHCyZ7bwt1lgo1Qa3NyQsVqBt1Mibt26n+iD2mRgLGmHzE5/Px9y583HmzGm0\ntbUiJcVxi4O7lufjk3O1ePf4BSzMTkZWYpzt57gcfG1REv5c2or9Nb1QaU3YMisevADuy9Y1qMc/\nK7rQrTZgeXo0FiRLPR5fVmPbxHBhdrLTY1arFXV1VxAXF4/Y2LiAxUhCr61HDYmYj6hIgfeDWW5m\nSjTqWlW42tGP/AzK06nG09oI+xd534AefQN6pCZIoNGZoBjQgc/nAFYLDBNQ58ukIoBhfO6q5+6O\n78nKTugNZsRGCTGkc90tkFqqs9fo9v0cBrC6uLgUCTi41jGAs1d6EBslBJcDmN3sETzgY9fIsXL3\n2tRYhAD+Na+x6xvRddRsseCdI/W42ODfc7gjk9hGpF0VbRwGSElwnEUU7t1OaYRtDNatKwYAHDjw\nodNjEpEQT2xdBYPJjJ+/ewRdypv7RMVF8PGdZSlIkAhwskmFP5xqQaPCuXmJv/QmCz6r68PvT7ai\nW23AqowYbJvjfqNswNYpcP+ZGnA5DFbnO2+Iff16F5RKBfLynDfQJOFLZzChR6VDijxyUjSSmXlj\nHVugNsck4Wf7+mwUF6UgLkoEhoHbfdQ0OhOee7QIS+dMg8E4McUaYGvCII8Rux35G3nxO6gx4NwV\n1xcvOoMZVtgueNxtdDxy5IOwy8iRCMD91ESdwTI8WtE3oHdbrAHuc3sk2TgKKz7PdeFPjUUI4Lng\ncYfDAGIhD3qjGW8cuIKSc+1uz2f+WpiXgGQ3SzuS5RK3e2S6Eg43JahgG4Ply1dBLk/A/v370NXl\nvNB3cU4qHi0uQs/AEJ56/QCudfUNPxYfKcD3VqRicWoUOgYM+HNZG14rb0ddrwYWV7ffPOjXmXC4\nXoFfHm3CoXoFRHwOHlmQiK2zXU9xHGlvWRXa+vpx6/wcyCTOdxQuXKgAABQUzPUrJsJuHb0aAOG/\nfs0uJzUGXA6Dy02KUIdCQsTekfHn31iCndvnub0wthc35+t9u7sbE8mHyI+9M+OiRCguSsH29dnD\nI3+uzM+JB4/L4O3Ddfjx389AOY6Ca2QnNsIenkYiOIyt9X6sVOhXfgHe16MJ+Rw8/dACxEjGNntC\nbzBjRf50xEWJwGEcc5oQTwWPOxYr8D9vVeDp/yv12nRHwLNdt3q7L8FhgHXzk7B9fTaeeXgBUhMk\nwzczOIxts/hnHl7g9HPezstsvylBUyLHQCAQ4NFHv45f/eqXeOmlX+Cll/4XPJ7jW3nvigJwOQxe\nO1iO77/6MR7btBibFuaCYRgIeRzcXzgNS1Kj8PGVPtR0a1DTrUGMmIfCE45yMQAAIABJREFU6RJk\nxYmRFiOCRMB1GAUxW6zoVhvQqNSi9nwXLncMwgpAxONgQ04s1mT41oGytKYZ/yypQEykCI/cstDl\nMWVlJwEAixYtGfsbRVinzb5+LWFyFGwiAQ/ZybZpka4WGJOpQ8jnIjM52u3ebDKpCAazxee7uzMS\no3GxwbkbsB0DIDZKiMLseBQvTEFslMjhC39kVz3FoA4xkULMu9FVz5fNk30RKeaz/iJjKvI0EmG1\nAjsfmAdppAA/fu2MX8/L5zJe96jS6k0eN5T/9tY5eOdwPVQumjXERolw//ps26jtja0oKL+I3Vjb\n83cqND4dZzDZctvb0IUVwMbFaeByOOByOPjJVxdjUGNAW7caKQnOI2sjuep2Wpgdh3Xzk6E3mlmd\n71SwjdG6dcX44osyHD9+FL/+9QvYufOHTkXbXcvykRQbhd/uPYE/f1yGE5eb8O3NS5F6Y8+zdJkY\n316ajBaVDmUt/ajqGsLxRhWON9qmdwl5HETyOeByGOhNFqgNZoc7bKkxQixKicKCJClEPiSZxWrF\nB6cu4Z8lFRDwuPjRA8WQip3vligUfTh3rhxZWTORlOS8vo2Er6Yu2xTd9Gme1zeGkzkZsahtVaGm\nWYnFswKzmJmEJ08XFPNz4iFwM+XLlZbuQcikApf7ZsVKhXji/rkeL2iHW1mbLThf3wulWo/Khl7A\nakXl1T6XP+Mvjc7I+ouMqcjTRsGxUSJkJkff+N++byYMABwOA3go2AwmC4wmCziM69E4DgPkpctQ\nNCvB5WckQsTDT98oD7t25yR4RhY8fQO6kMQQ62L6ojRCgFkznLenGm3kHpmKAR0On21FZUMvPq9o\nZ33OU8E2RgzD4Ikn/h96e3tw7FgJ1OpB7Nz5NGJiHDegXpKbhj99ayv+8nEZyuvb8B8v78WmhbnY\nvmouYqURYBgG6TIx0mViGPMtaFRo0azSoa1fD4XGCI3RdgLmcxikxYiQIBEgNVqEZblyQOfbBpxW\nqxWXmrvwj8PnUNveg1hpBH5431rkprgeGt67932YzWbcdtuWcb9PhF2auwbA4zJIDvMOkSPNyYjF\nB8ev4dK1PirYiMf9okxmK0QCrk8t/lWDeiydM93lHm4LcuU+TSveVdLg0MK6b0DvtaW1kMeB3k3H\ntdGUg3rWL5SfirzdOLAX2P6MViyYGY/z9e5HfAHbhSyfx/HYOU+rN7n8jESIeA77WYVTu3MSPCML\nni7FEP7nrYrhbSaCJRDTF4V8Lo6eb3c6P7M556lgGwexWIyf//wl/PKXz+Ps2TN4/PGv4/HHn8Cy\nZSscpjLKoyV47sFifFHbgtcOlePj8is4dL4eG+bn4K7lczAtxjbawedykCOPRI4PF9NyqRA9Xgo2\ng8mE0ppm7D9Tgytttvn0q+Zk4FublyI6UuTyZ7q7r2PfvvcRFxeP9es3+PpWkDBgMlvQ2q1GilwC\nnoftHsJN+nQpoiUCXKjvhclsmVS/G/GPfS+ze9ZkudwvissBVhRMx5Fz3jcSlklF2HHrTMTLInDq\nYoffmwV7W8fk6qI6RiLAc48uwoHTzcMX0zESITR6k8siMxwWyk927vbP82WjaYdpswM6MG7yIi5K\niEc256H5ernHEbn5OfGQyyIQ62FkOFoidLjo7lfrIRbaRtZcoU6kxBUhn4v0aVFYNTdpXNO7hXzb\n97Wros9+nrT//1ipEAty5QFZU+mtxT8bc54KtnESi8X4yU9ewJ49u/Dmm3/Hz372I8ydOx9f+cpj\nyM3NGz6OYRgszUtH0cxUHL5Qj/dOVGJ/eQ0OnL2CJbmp2FyUh3mZSV6bhXhjtVpR296Doxev4ljV\nNahvFHWLc1KxffVc5Ca7HlUDALPZjN/97iUYDAY88sjXIBK5LupIeGrvGYLJbMWMxKhQhxJQHIZB\nUU4CjlS04UqLktr7T0GjN3P1NLXlgVtmgmEY27GDegj4HOhdrGubnxOPCCEf39hWgM2LU/3eLLhf\nrXd7ce1uBKQoLwExEqHTBsWjN+EeGSPbLiqmCrPFglf2XsKpi+0uc250UeQqd0Yf81l5K45WON9M\niBDxESHiuR2REwm42LAkHXcss63rWZDresrjgly5Qwz2PdW6lZqwbndOQmf0jQkB37cZDNNlYnzj\nzlk4WdmFsmrXzUjWzE/GgxtnQTukg1ZvCuhm7eHY4p8KtgDgcDi4//4HsWzZCvztb3/G2bNn8MQT\n38bixcuwffsOzJ6dP3wsj8vBpoW5KJ43EyeqG7H3dDXKrrSg7EoL5FGRWFOQiRWzZyArMc7n4s1q\ntaK+oxelNc04eblpeCuBmEgx7lmej40Lc5EU6/ki3Wq14rXXXsaFCxVYsmQ5ios3jv0NIazU1DUA\nAJgxffKsX7NbNMtWsJXXdFPBNgW528sMcJ7aMvoiWRLBx94TjR5HQuwXtv6IlgghEnBcNjkR8jlY\nXpCIyoY+n15z9EVRfIwYhVlx1L0vhHzNOV9yx37MjuKZaGjrd5iaCACt3WrsKmlwyoMYiRB56TLs\nuHUm0lNi0dNj++73ZXRvJE9r7mgUl3ji/XwqhN5ohlrruI9Kl1KLNz6pc8p1wHYDYmVhIravz8b0\n+Ej0WC0BbygWjjlPBVsApaam4Wc/exGVlRfwz3/+HWfOlOHMmTLMmVOAe+99AIsXLwXnxt1eHpeD\ndYVZWFuQibr2XnxWUYsT1U3Yc+oS9py6hKgIIRZmp2BWSgLSp8kwLUYCiUgAMAw0OgO61GpcqG3H\n5dbrOH+1A0q1bT83sYCHdYVZWJOfiflZST4tnDSbzXjttZfx73/vQWpqGr7//f+eFHt0EUeNnZOv\n4Yhddko0oiUCnKvtwY5bc2jUYQoZ69SWkRfS3kZCxs71eZRhGNy/Lhv3r8v26TVHXxRlzYjDYP/4\n9/AkYzNR06lMZis0XjZJ9yVXfRndG8nXNXeEuOPufGqfbju6YAOA9h7nYg0AIoQ83LMma0Ibf4Rj\nzlPBNgEKC+fhV7/6PaqrL+G9995GefkXqK6+hJSUVNx99/245ZYNEAhsdwsYhkFuihy5KXI8tnkp\nKhracLq2BReudeJo5VUcrbzq9fWiI0RYPzcLS3PTsTA7GUK+73/Wvr5e/OY3L+L8+bNITU3DCy/8\nBlLp5LugJ0B9mwpCARcpCZOn4Ygdh2GwsiARH5c140zNdawqTAp1SCRIAjW1ZSyjaN7i0ruZGmS4\nse4pQRbh12vaYxQJeBgMVKDEbxM1ncrX5/U1V/3JaX9H5QjxxJfptu6mhqvUwWmmFG45TwXbBGEY\nBvn5hcjPL0Rj4zV88MF7+PzzI/jDH36DN9/8O7Zs2YYtW7YiKip6+GdEfB6Wz5qB5bNmwGK1orVH\nhfqOXrT2qNAzMIShG+vRIoR8JCfEQC6JRG5yPNISZH6vfTMYDPj44314661/QKMZwuLFS7Fz59NU\nrE1SAxoDOvs0mDNDxsp2tYGwdl4yDpxuxtGKdirYphC2Tm1ha1xk/CbqbxvKnPF3VI4QX3jKaXfN\nl4J1fgy3nKeCLQgyMjLx/e//Nx599OvYt+8DHDjwId5883Xs3v0ObrvtDmzbdi/k8gSHn+EwDNIT\nZEhPkLl8TrlcOjxf3R9DQ0M4fPhTfPDBbnR3X0dkZCS++93/wqZNW4ana5LJp761HwAwMzXGy5Hh\nKy5ahHnZttbXl5sUmO3DniyE/dx14bNj69QWtsZFxm+i/rYT8bzePj+uYmBbswUSvjzldLJc4nIN\n2+hc9zeHxxJjOOQ8FWxBFBcXj69+9TE88MCX8dlnH+P999/DBx/sxr59H+CWWzbg/vt3IDk5ZUJe\nu7W1BR9/vA8HD34KrVYDoVCIu+66F9u3fxnR0dHen4CEtfo222bsOSmTt2ADgDtWzMD5+l68f+wq\nZqXLaC1mGPOn8yNbp7awNS4yftvXZyNCLBjTlg/enhcYf8748/khZCK5y+l712Ziz+fX3Oa6t06s\nUw1jtVrdzCJlh7GMIo3XWEev/GU0GvH550ewe/c7aG1tAcMwWLFiNR588MvIzPR8cvYlRrPZjNOn\nT2Hv3vdRVVUJwFY03n77VmzevMVpk+9AC9b7OPL1Qi0U+erK6Pf+J2+Uo61bjT/91+qQ3dkPVj78\ndW8Vyq904xtbZmNZ/vSQx+NJqHM21L//SKP/Hm8frnN5V7a4KMXtpqYTcSc2EHkS6LhClbuhzlc7\ntuStXC5FW4dqQu7++5szgfj8BAobzq12oc5ZV+8Dm94fu2DE5C6n3f17KHPYk4l8rzzlK42whRCf\nz8ett27C+vW3orT0BHbvfgcnTx7DyZPHsHDhImzbdi8WLlzk9yiBWq3Gp5/ux/79+3D9um1/i7lz\n5+P22+/EsmUrwePRn30qGRgyoLlrEHlpMVNiGta9a7NQebUP/zpUh7x0GWRSWisUbgLR+ZFN2BoX\nGb+J+tuO53nDcVNgMvm5y2lX/0457CyoV+4mkwnPPPMMWlpaYDab8dRTT6GoqCiYIbASl8vFqlVr\nsXLlGpw7V4733nsb586V49y5cqSmpmPjxtuwevVap3VuI1mtVtTW1uDQoc9QUnIQOp0OQqEQt912\nB7ZuvQdpaelB/I0Im1Q19gEACrKmxv5k8hgx7l+XhTcP1uHlfVXY+cB88HlTb/pEOAvHTU1Hm+h1\nF4S406PShv3nh0xtwfgOCLdzdFALtn379kEsFuOdd95BfX09fvjDH2LPnj3BDIHVGIZBUdFiFBUt\nRn19Lf797z04ceIYXn31r3j11b8iLS0d8+cXIS0tHWlpiejv10Ch6MO1a1dRUXEW3d3XAQByeQJ2\n7HgYmzZtoa6PBJVXbxRsmVOjYAOAtfOTUduqwpmabrx+oAZf3zIbHA6tZwsX4dxhkdYOkVCx515F\nbTfcrXVh++eHEGBivwPC9Rwd1ILtzjvvxJYtWwAAsbGxUKlUwXz5sDJzZi6eeuoZfPOb/4GTJ4/h\n1KnjqK29gn373nd5fGRkJNatK8batbdg4cJF4HLZf7eATDyzxYLqRgVio4RIjp98+6+5wzAMvnrb\nLPQN6HD68nXweRw8sjnP7+0vSGiEc4fFXSUNDnH3DeiH/zuU6y7I5Dc691xh++eHEGBivwPC9Rwd\nsqYjv/3tb8HhcPDEE094PM5kMoPHo5MLAOh0OtTV1aG1tRX9/bY27bGxscjIyEBWVhatTWMBtuXr\nuSvX8fwrp3H7igx86+7CUIcTdGqtET96+RQa2vpRvCgN371/Ho20jcC2fB3JbLbg7x9V43RVJ3pV\nWsTHiLE0PxFfvWMOuNzA3wXVGUxQDughixJCJPB8LnV3rM5gwuMvlaBbqXX6mQSZGH9+ar3X5yYE\n8C8f7ce7yz3ANlW8IDsej23LR6RYEOhwiRtsPseGiq+57eo7oGjWNNyxKhPxMeIxnUvD+Rw9YVHt\n3r0bu3fvdvi37373u1i1ahX+9a9/obq6Gi+//LLX51EqNRMVolts7OBjl5iYgcTEDKcYlW5O0qE0\nFbtEhiJfXbG/95+VNgEA5mbGhjynQ/W5+t69hfjNuxdwuLwFRqMJD2/MBcMwrPichzpn2ZKvgOv8\n2LZiBjYvTnVYZ6BQDAX0db1NjxkZl7dju5Ua9Lg5F/eqtLja1BeQtUPUJZId388T8XcY63QtE8Nx\nm3sAYDCacPRsKy7WdU/49C82nFvtQp2zrs6xbHp/7IIR01hy+xvbCrB5cSoUAzocPtuKL6o68Ulp\n05inMQbiHD3pukTed999uO+++5z+fffu3SgpKcFf/vIX8Pn8iXp5QqY8vdGMivoexEeLkJUUFepw\nQiZSxMfOB+bhpXfO49iFDkgj+Lh7dVaowyI+mugOi/5Mj/F2bDivvSPsMNbpWrIo97kHAP1DRr+e\nj5BAG2tuC/lcHD3fjqPnO/z+2dHC+Rwd1NV1ra2tePfdd/GnP/0JQiF73xRCJoPymm7oDWYsnTNt\nym8gHSHi48nt85AQI8b+0maUVnWGOiTCAt5aR+uNZr+Ota+7cIXWDhFv/MnH0UQCntvcG8vzERJI\n48nt8fzsaOF8jg5qwbZ7926oVCo89thjeOihh/DQQw/BYDAEMwRCpgSr1YpDZ1vBYRismZsc6nBY\nISpCgO/dVwixkId/flaLlq6BUIdEQsyX1tH+Hrt9fTaKi1IQFyUChwHiokQoLkrB9vXZgf8FyKTi\nTz66Mjr3ZB5GC3x5PkICZTy5Pd7PxWjheo4O6sq6J598Ek8++WQwX5KQKanqah9au9UoyktAXLQo\n1OGwRmJcJL6yOQ9/2VuFX711Dk9/eQF4E9DAgoQHf6bH+Hosl8PBjuIc3LMmK6z2+CGhN97pWqNz\nTyzk4advlIfl9C8yuYwntwM9jTFcz9F0pULIJGO1WvH2wSsAgA2LUkMcDfsU5SVg9dxENHUO4EBZ\nc6jDISHkz/QYf6fS2NfehcOFAGGHQE3XsueeNEIQttO/yOQyntyeqGmM4XaOZmfvSkLImFU3KlB1\ntQ+FWXHITo4OdTisdP+6mahuUuKj0iYsmpWAxLips0cdcWSfBnO+rhfKQR1kUhHm58S7nB7jz7GE\njEWgc4xylrDFeHKR8jiE+7D5KlSti9nWcnU0itH164VaqP8mJrMFP3mjHO09Q3j+K4uQNi3074kd\n23K2oWsQv3yjHHMyYvHk/XND0pgl1DnLpr9HqPNDbzS7nB7jKi53xwYLtfVnR95O5N/B3xzzFksw\nczbUn+WRQp2zrt4HNr0/dsGMyZ9cHB1XqM+9rmIK9HO7Q1MiCZlEDp1tRXvPEDYtm8GqYo2NluYn\nYs4MGaobFbjY0BfqcEiI+TM9Jtym0pDwE+gco5wlbDGeXJzKeUwFGyGTRGffEPaeaIQ0go9HbpsV\n6nBYj2EYPFCcA4YBdn/eALPFEuqQCCGEEEKcUMFGyCRgMlvw6v7LMJoseGhDLiQRglCHFBaS4yOx\nem4SOvs0OH6R9mYjhBBCCPtQwUbIJLDvZCMaOwexbM40FOUlhDqcsLJtZQYEfA4+PNkIvYE2kiWE\nEEIIu1DBRkiYu9ykwIGyZshjRPjyhtxQhxN2oiVCbFiUhv4hAw6dbQ11OIQQQgghDqhgIySM9Q8Z\n8LePLoPDYfDNO/MhFtJOHWOxeUkaJGI+PvmiGWqtMdThEEIIIYQMo4KNkDBlsVrxykfVGBgy4N61\nWchMigp1SGFLLORhy7J0aPVmfFzWFOpwCCGEEEKGUcFGSJg6UNaMy01KFGbF4dZFqaEOJ+ytW5CM\nuCghjpxrQ69KG+pwCCGEEEIAUMFGSFiqa1Xh3yeuQSYV4mu3zwInBJs+TzZ8Hhd3r8mCyWzFnmNX\nQx0OIYQQQggAKtgICTuDGgP+78NqMGDwzTvnQEot/ANmyexpyEiU4kxNN+paVaEOhxBCCCGECjZC\nwolt3dplKAf12LYqAzmpMaEOaVLhMAweLM4BALx1sI420yaEEEJIyFHBRkgY2XeiEVWNChRkxuG2\nZemhDmdSyk6OxsqCRLT1qHGwnNr8E0IIISS0qGAjJEycq+3GR6VNiI8W4Rt3zKZ1axPovnVZiIrg\n49/HG9HZNxTqcAghhBAyhVHBRkgYaOwcwCsfXYaQz8V/3F0AiZgf6pAmNWmEAA9tzIXJbMFf91bD\nYDSHOiRCCCGETFFUsBHCcu09avzuvYswmi345p1zkDZNGuqQpoSFuQlYOy8JbT1q/OPTK7BaraEO\niRBCCCFTEBVshLBYY+cAXnz7PNRaIx7dlId5M+NDHdKU8mDxTGQmRaGs+jp2lTRQ0UYIIYSQoKOC\njRCWKqvqwotvV2BIZ8Qjm3Kxam5SqEOacvg8Lr53byGmx0bgYHkrXv/kCowm6hxJCCGEkODhhToA\nQoijLoUGu4824Hx9L8RCLr55VwHm58hDHdaUJY0Q4AdfWoD/3X0RJys70dQ5iIc25mBmCm2pQAgh\nhJCJRwUbISygHNTjSrMSZ2quo/JaH6xWICc1Bl/ZnIdpsRGhDm/Ki44U4L93LMC7JfU4dqEDL7xV\ngby0GCybMx1zMmIhkwrBUNdOQgghhEwAKtgICbLyK924dK0PGp0J/Wo9ulVaDGqMw4/PmC7FbUvT\nsTBXTkUAiwgFXDyyKQ/L86dj74lG1DQrcaVFBQCQRvCRECNGtESICBEPRblyFGbRekNCCCGEjB8V\nbIQE2Senm9HUNQgA4HIYxEYJkZ0cjezkaBRmxSFZLglxhMSTmSkx+H8Pzsd1pQYX6ntR39aP1u5B\nNHUNwmwZAACo1Hoq2AghhBASEFSwERJkP9ixACq1HhEiHiLFfNoAO0xNk0Vg4+I0bFxs+2+L1Qq1\n1gitzoTYKFFogyOEEELIpEEFGyFBJhRwaV3aJMRhGERFCBAVIQh1KIQQQgiZRKitPyGEEEIIIYSw\nFBVshBBCCCGEEMJSVLARQgghhBBCCEtRwUYIIYQQQgghLMVYrVZrqIMghBBCCCGEEOKMRtgIIYQQ\nQgghhKWoYCOEEEIIIYQQlqKCjRBCCCGEEEJYigo2QgghhBBCCGEpKtgIIYQQQgghhKWoYCOEEEII\nIYQQlqKCjRBCCCGEEEJYigo2QgghhBBCCGEpKtgIIYQQQgghhKWoYCOEEEIIIYQQlqKCjRBCCCGE\nEEJYigo2QgghhBBCCGEpKtgIIYQQQgghhKWoYCOEEEIIIYQQlqKCjRBCCCGEEEJYigo2QgghhBBC\nCGEpKtgIIYQQQgghhKWoYCOEEEIIIYQQlqKCjRBCCCGEEEJYigo2QgghhBBCCGEpKtgIIYQQQggh\nhKV4oQ7Am56ewaC/pkwWAaVSE/TX9QfF6EwulwbttdwJRb66wsb8YFtMbIgn1DnLlnwF2PH3cIWN\ncYUqplDnKwCYTGbW/D3YlBsUi2uhzllX51g2vT92bIwJYGdcExmTp3ylETYXeDxuqEPwimIknrDx\nvWdbTGyLZ6pj69+DjXGxMaZgYdPvTrG4xqZY2IiN7w8bYwLYGVeoYqKCjRBCCCGEEEJYigo2Qggh\nhBBCCGEpKtgIIYQQQgghhKWoYCOEEDJlDemMaOwcgEZnDHUohBBCiEus7xJJCCGETIQLDb14bf9l\nDOlM4HE5+MrmPCzLnx7qsAghhBAHNMJGCCFkyunoHcKfP7gEg8mC1XOTwOdx8Mr+yyir6gp1aIQQ\nQogDKtgIIYRMKVarFW8drIXZYsU375yDRzfn4ekvL4BIwMWbB2vRq9KGOkRCCCFkGBVshBBCppSa\nZiWutKhQmBWH+TPjAQDJcgm+dGsOdAYz3jpUF+IICSGEkJuoYCOEEDKlHL/YAQC4fVk6GIYZ/vfl\n+dORlxaDyqt9qGlShCo8QgghxAEVbIQQQqYMtdaIirpeTI+NQHZytMNjDMPg/vXZAID3Pr8Kq9Ua\nihAJIYQQB1SwEUIImTLKr3TDZLZg1dxEh9E1uxnTo7AoLwHNXYO4dK0vBBESQgghjqhgI4QQMmVU\nNvQCABblJrg95o7lMwAAH55qolE2QgghIUcFGyGEkCnBaDKjplmJpPhIxMeI3R6XkiDBghw5rnUM\nDBd4hBBCSKhQwUYIIWRKqG1RwWCyoDAzzuuxty9LBwDsOVI/0WERQgghHlHBRgghZEqovLEmrSDL\ne8GWkRiFWekyXKjvQWPnwESHRgghhLhFBRshhJApobZFBT6P49Qd0p3bboyyffJFy0SGRQghhHhE\nBRshhJBJT6Mzoq1bjczEKPB5vn31zU6XISslGueudOO6QjPBERJCCCGuUcFGCCFk0mto74cVwMzU\nGJ9/hmEY3LNuJqygUTZCCCGhQwUbIYSQSa+utR8AkJPq23RIu+WFSUiQiVFa1QnloH4iQiOEEEI8\nooKNEELIpFfXpgLDAFlJ/hVsXA6DzUvSYDJbcai8dYKiI4QQQtyjgo0QQsikZjJb0Nw1iFS5BGIh\nz++fX56fiGiJAEcvtGNIZ5yACAkhhBD3qGAjhBAyqbX3DMFosiAjKWpMP8/ncbBhUSr0BjOOVrQH\nODpCCCHEMyrYCCGETGr2fdQyEsdWsAHA2nnJEAt5OHS2FQajOVChEUIIIV5RwUYIIWRSC0TBJhby\nsH5BMgY1RpRWdQUqNEIIIcQrKtgIIYRMao2dAxDwOUiKjxjX8xQvTAGPy+DTL1pgsVgDFB0hhBDi\nGRVshBBCJi29wYz23iGkT5OCyxnfV160RIjl+dPRrdLifH1vgCIkhBBCPAtJwfbSSy9h+/btuOee\ne3Dw4MFQhEAIIWQKaO1Rw2oF0qdLA/J8ty5KAwAcKqeNtElo6I1mdCs1GNQY0K3UQE9rKkmQ2XPQ\nXe55e5z4z//+xuN0+vRp1NfXY9euXVAqlbjrrruwYcOGYIdBCCFkCmi5PggASJ8WmIItOT4S+Zmx\nqLqmQGPnwLjWxRHiD7PFgl0lDThf14O+AT04DGCxArFSAVbOS8Edy9LGPYpMiCcjc1AxoEdslBDz\nc+TYvj4bXA7H6+Nk7IJesC1atAiFhYUAgKioKGi1WpjNZnC53GCHQgi5wWg04tSp4ygrO4Wenm4I\nBAJkZWWjuHgTMjIyQx0eIWPW3BXYgg0ANhSlouqaAiXn2vC1LbMD9ryEeLKrpAGHz7YN/7d9GaVi\n0IAPT1yDRmvAjuKcEEVHpoLROdg3oB/+7x3FOV4fJ2MX9IKNy+UiIsK28HvPnj1YvXq1x2JNJosA\njxf8Yk4uD9yXuzdqtRo1NTVoaWmBUqmEyWSCUChEXFwc0tPTkZOTA7FYHNIYxyocYgykUOWrK76+\n93V1dXj22Wdx7do1ALbPqMViwcWL5/HBB7uxZcsW7Ny5ExKJJGgxBQvb4gk2NuUrMDF/jw6FBnwe\nBwV508Djju0O7+i41sRJ8G5JA85c6ca375uHaIkwEKGOK6aphE2/e7Bi0RlMqLza5/GYyqt9+OY9\nYogEQb+0c8Kmv1EouTvHsvH98RaTpxysvNqHR+8QeHx8rLkZju9BYXp6AAAgAElEQVTVRAjZp/rw\n4cPYs2cP/v73v3s8TqnUBCmim+RyKXp6Bif0NfR6PUpKDqGk5BAuX66CxWJxeyyXy0Vubh6WLFmO\nVavWIjExKSgxjlewY2TDhzoU+eqKr+/9lSuX8cwzT0GjGcKmTbdj27Z7kJY2A3q9DhUV5/DOO//E\n/v37cfHiJfzP//wGsbFxEx5TsLAhnlDnLFvyFZiYv4fJbEFTxwBSEyRQKoYCGteauUl450g99h6t\nx21L08cbakBiCsbrskGoP7d2wfw7dCs16FFqPR7Tq9LialMfEmTj64Y6Xmw4t9qFOmddnWPZ9P7Y\n+RKTpxzsVWlxsabL4+PuclNvNKNfrUe0RAgh37G4Ddf3ajzP7U5ICrYTJ07g5ZdfxquvvgqplB1f\nAMFitVpx8OABvPHGa1CplGAYBrNmzcacOYVIT5+BmBgZ+Hw+9Ho9+vp60dzchJqaaly5UoPLl6vx\n+uuvID+/EA8+uB0FBYvA5/ND/SuRMKVQKPD8889Ap9PiBz/4EdauXT/8mEgkxvLlK7Fo0RK8+upf\n8eGH/8bTT+/Er3/9x4CMtBESDB29QzBbrEgL4HRIuxUF0/H+sas4frEDm5ekgWGYgL8GIXbREiFi\no4ToG9C7PUYmFYVktJdMDZ5yUCYVISVB4vHx0blJ6938E/SCbXBwEC+99BLeeOMNxMTEBPvlQ0qr\n1eK3v30RJ08eg0gkwvbtO7BlyzbEx8u9/uzAQD9Ony5FSckhXLx4Hs88UwmZLBa3334nbrvtDshk\nsUH4Dchk8te//gH9/So89th3HIq1kfh8Pr71re/CagU++ujf+N3vXsKzz/6ELk5JWGi5rgYApE8L\n/E2GCBEfRXkJKK3qwpUWFWalywL+GoTYCflczM+RO6wPGm1+TrzDCIWnkQtC/OUpB+fnxEMaIfD4\nuD0H7Xn5WXkrjla0Dx9D6908C3rBduDAASiVSjzxxBPD//biiy8iKSkp2KEElcFgwHPP/TeqqiqR\nn1+IH/zgWZ8KNbuoqGhs2LAZGzZsRnt7G0pKPsG+ffvw1ltv4N1338LKlauxefMdKCiYSxfTxKvK\nygs4efIYZs+eg61b7/F4LMMw+OY3H0dzcyNKS0/g448/xJYtW8cdg9VqRWdnB5qbG9HX1wuDwQCR\nSAS5fBqysrLHNf2SEABo7bYVbKkJEzOTY/XcJJRWdeHYhXYq2MiE27YqAycrO6EzOLdKFwt52LYq\nA4DnkQuT2UpFHBmz7euzAQDn63qhHNRBJhVhfk788L97etxVl1NXztf14p41WSHLT7be6Ah6wbZ9\n+3Zs37492C8bcn/96x9QVVWJlSvX4KmnnhnXVMbk5BQ8+eSTuPfeL+PIkc/w4Yd78fnnJfj88xIk\nJ6eiuHgD1q0rxrRp0wP4G5DJZNeufwEAvv7174Djw9QDLpeLp556Bt/61lfx6qsvY8GCIiQlJfv9\nuiaTCRUVZ3Hy5DGcPfsFlEql22MzMjKxdu0t2LDhtik3Gk8Co7Xbts4gWR45Ic8/MyUa02MjUFHX\nC43OiAgRTVEnE0etMULvolgDAL3BBLXGiAgh322nvtoWFTQ6I00/I2PG5XCwozgH96zJclnUeHr8\n7cN1LrucjqYc1KFfrQ/6Wky2T9EMfSuhKaCy8gI+/fRjZGZmY+fOHwZs3ZlYLMaWLdtw++1bUVVV\niU8+2Y9Tp47jH/94Df/4x2uYM6cA69cXY+XKNYiKig7Ia5Lw19zchIqKsygomItZs3xvSR4XF4/H\nH38CL774M7z00s/x61//ETyeb6cQlUqFvXvfxe7de6BQ2LpIyWQyrF27HhkZ2UhISIBQKIRWq0Vn\nZwdqaqpRWXkBr7/+Ct5++01s3Xo3Hnjgyy67pRLiitVqRWu3GgkyMcTCifmqYxjmxlq2azhT0421\n8/2/iUGIO6Pv9HtaQxQfI0a0RAi90YzzdT0un88+4gzQ9DMyPkI+d7igcjUiNfJx+zHu8nK0UK3F\nZPuWBFSwBcGbb74OAPjP/3wSQmHgk5BhGBQUzEVBwVwMDX0PJ08ew9GjR1BZeQHV1Zfwl7/8AYsW\nLUFx8SYsWbLM54tsMjl9+unHAIA777zL759ds2Ydzpwpw9Gjh/Hyy3/E448/4XEKbldXJ95/fxcO\nHvwEBoMBERGRuOOOu7BuXTFyc/M8ju4NDg7iyJHPsGfPLrz33tsoKTmEJ5/8AebPX+h33GTqUQ7q\nMaQzIW+Cpyouz0/EB8ev4eSlTirYSEB4utPvbo3Q0vxECPlcdCs1UHhoTDJaqKefkfDlz4hUv1rv\nc16OXosZDJ4KSrZ8RujKfYI1NzeiqqoSCxYUITd31oS/XmSkBBs33o6NG29HT08Pjh07gqNHj+D0\n6VKcPl0K2f9n77zj2yrv/f8+2pJtee8Rz9gZZO/BCCEhQCillEAo0EsHtwN6C7S/25S2lO5CS0tp\ne3uhLYVCSS/ccqEUAiEhg+w4e3gl8Yq3JdmytnR+f8iyLWtYdjzk5Lxfr75KdB6d80h6zvHzXZ9v\nYhI33ngzt9zyCalG6ArE7Xbz0UcfotfHs3jxsmG/XxAEHnroEc6fr+Gdd94C4POf/xIajaZvjMfj\n4dSpE/zzn2+ye/dOPB4PGRmZfOYz97Bs2aqIo2RxcXHcdtsdrFu3ns2bX+Hvf3+VTZseY+PG+7jn\nnvsjSuWUuHKp66tfG1tV08Q4NdPzkzh1vpPmTgsZSRMrqS4x+Qnn6Q9VI/TA+hl0dvZEpCY5kIlK\nP5OY/AwnIhVuXcoEEIGkODVleYnctrJwTOcdjHAGZbTcI5LBNsbs2LEdgDVr1oUd53S7+fBoNfsr\n6rjY2YVcJiMnJZ55RdmsmJFPrGb4kbnU1FTuuOMu7rjjLs6dq+a99/7Ftm3v87e/vczrr7/GmjXr\n2LDhM6SmRi5+IjG5OXr0MEajgfXrbxtxaq5Wq+VHP3qKxx//Ju+88xb79u1h8eKlxMXpaW9v48SJ\nY7S2tgBQWFjEpz61gWuuWUVGRsKIepeo1Wruu+8BlixZzk9+8n1effUl6upq+cY3NqFSqUb0GSQu\nf+rHyWADWDYjg1PnO9l3qnlCNhsS0clIxAsi8fQHqxGS9zaFVyvlzClJ4cPDjUHPMRipFYDESBhJ\nRKosL5GPTzYHjL96TiYOp8jZ2s5e1V1DX6TO5nDRarCMuQDIUC0LouEekQy2MWb//r0oFEoWL14a\nckxNUwc/+Z/tNBu8m1m9To3bI1LXZmTPmVpe2HKAmxaUcceKq9DrNCHPE47CwmK+/OWHeeCBL/Dh\nhx/wxhubeeedt/jgg/f49Kfv5s47N0qb3yuAnTs/AuDqq4PL+EdKUlIyv/jFc/z976/yj3+8zr/+\n9XbfMZ0uhuuvX8OaNetGVbV06tRSfv3r3/PDH36P3bt3YLPZ+M53npTWrURQGsbRYJs7NQWVUsbe\nU818YkWBpNR7hXMp4gWRevoH1wgNxCOGUHMIwkSkn0lMfiJdp4OVITUqGSDgcLr7osMeUWTPyUB5\n/4o6I3anmzaDdcyVTodqWRAN94hksI0hXV0mzp2rZvbsuWg0wdPAqpva2fSX97DanaxfNI3bl80k\nNT4WURRpMZrZefIc/zx4lv/de5L3j1Sy8dq5rFtQOuI5aTRabr75Vm688Wa2bt3CSy/9iVde+Qs7\nd37EN77xLUpKRn5uiejG5XKxd+/HJCenMH36jEs+n1ar5f77P8fGjfdRV1eL1WolPj6erKxs5PKx\nebjp9fH84Ac/44c//C6HDu3n179+msce+5a0QZYIoKHNjFatIFk/MifXcNCoFMybmsq+Uy2cu9hF\nUbYk8nQlcyniBZfq6bc73ew92RLyuEzwqvMlxamZV5rKbSsLxiWCIXF5Eek6HXwv2BweABZPT+fm\npVOIj1Hx5IsHg14jmEjOWCqdDtWyYKKRDLYx5NSpEwBcddXsoMdtThc//Z+PsNqdPHb7NVxzVX8q\njSAIZCTGcefK2Xxy6UzePnCG13Ye5b/f28/b+0/zxfVLmJObiVIxsgesXC5n7dqbWLnyWl588QXe\nfvsfPPLIQzz88CPccMONIzqnRHRz4sQxuru7WL/+k6Na/6VUKikqivyB1mo0s/v0BU7UNlPfZsTU\nYwMgWa+jJCuFJaV5LC7NQyEPPke1Ws3jjz/Jf/7nI2zb9gElJVO57bY7RuWzSFweOJxumjstlGTH\nj5sxv2R6OvtOtbD/dItksF3BXKp4waV6+tuM1qB92nz4pNSvKvbWsH/vjweiUsJcIrqJZJ2GuxcO\nnmnhwOkWEmLVGMyRi+SMpdLpUC0LJhrJYBtDTp48DsDMmbOCHn9tx1GaDd3cvnSmn7E2GKVCzu3L\nZnL97GL+tuMo7x2u4PsvfUC8TsOyaVOYW5TF1OxUkuN0w96c6HQ6vvzlh1m8eAk//ekP+eUvf0Zn\nZwcbNtwzrPNIRD/79n0MwNKly8f92qIosu9MLS++d5AjNRf7Xk+I0ZKZFIfHI9Jm6mH78Rq2H68h\nVR/DA2sWsmJ6ftA1rVar+c53fsCXvvQAf/7z8yxcuITs7Jzx/EgSUUxjew+iCDnjkA7pY3p+ErFa\nJQfOtnLX9SXIQnWFlbisiSRVLD5WHXZDeEme/gjTIfefavEz7KJNwlwi+hm8ThNi1ZRNSexr4B7u\nXvA5DoZjrIVitFUcw6UbTySSwTaGHD16BKVSSVlZYK8rk8XG2/tPk6LXsfG6uRGdLz5Gw7/ftIRP\nLpvBhydq+Oe+07x7uIJ3D1cAoFMryUrSk50cT05KPIUZSZTlpBEfM3RK0Pz5i3jmmd/y7W9/gxdf\nfAGlUsntt985vA8sEbWIosiBA/vQ6WJCRnzHijP1Lfz5g0Ocrm8FYEZeOtfPLmZecTYp+v6Gxh5R\n5EJLJx8cqeK9w5X87PWPODynhK/cvDRoJDkpKYkvf/lhfvrTH/CXv7zApk1PjNdHkohyxlNwxIdC\nLmNBWRofHWnkTJ2BGflJ43ZtieghXKpYQqyaLQfrOV7dHjaqdSme/tREHRqVrC/1LBShonDRImEu\nEf341ultKwv52weVnK0zsPdkMxW9oiG3rSwclmLpSIkWFcexRjLYxgij0dBXvxas99q7h85id7m5\nb+lMNMrh/QzpCXF87faVbFg+izP1rZyua6GmuZP6NiO1rUaqmzr8xhdlJrOsbArXzS4iLT70BiYn\nJ5ef//xXPProQzz//O/JysphyZLhS79LRB+NjfU0NzexYsU149aHr9nQzZ+3HuLj0xcAWHlVAZ9a\nMpPirJSg42WCQGFGMg+uS2b94uk89cYOth6twmSx8e07VwVNkbz66ut4442/s2vXDpqbm8jIyBzL\njyQxSfAJjoxnhA1g8TSvwXbwTItksF2hhEsVi9Eq2V4eKK4AwaNaI/H0q5Vyll2VybYIVSIH09lt\no81oJSd1fO8dicnLm7vO+ak/DlzXoe6FYCTEqujqcZAYp0GnUfilP4YjWlQcxxrJYBsjDh06AMD8\n+QsDjrncHv518Cw6tZIb5o489UAukzFzSgYzp2T0veYRRdpNPdS2Gai+2MGJ2mZO17ZQ09TBKx8d\n4ZqrCrn3unmkJQR/GKenZ/DEEz/m0Ue/yi9+8RN+//s/kZIiyf5Pdg4fPgQEX4+jjcFs5X92H+df\nh87icnsozU7lc2sWcs384ohl/bOS9Pz0s+v44eYPOVhZz/Nb9vOlmwKVVgVBYP362/jlL3/GRx99\nyF13fWa0P47EJKS+1YwA5KSM76azJDeBhFgVhyva+Mya0pB1mBKXN8FSGmcVJXG8piPo+MFRLbvT\nTZvRCqJIaq8i5HC4+/oSZIJAeUUbnd3BoxsalTxolE0U4ZevHWVuaSobV5dI9WwSYQlXp1Ze0cas\n4uSQa20gibFqNt07D7dHJD5WjUIusHlbNcdrOmg3WsMacYNrO4fbTmMk7TcmAslgGyP27NkNELQ5\n8cGqejrNVtYvmoZOPbJeWKGQCQJpCbGkJcSysCSXu4Eem4OPT1/g//afYvvxGj4+fYH7r5/PrYun\nB60PKi4u4cEHv8JvfvMMv/nNM3z/+z8e1TlKjD8nThwDYM6ceWNyflEUqWnu4L3DlWw/Vo3d5SY9\nIZb7Vs3n6pkjkzlXKxVsunMVj/3xn7xz8CxLSvOYW5QdMM7XMuPo0XLJYJNAFEXqW82kJWpRq8b3\nj69MEFg0LZ33D9Zz6nwns4uDR5MlLm+CpTSazHY+OnIx6HhfSldyvIbXPqzi4xPNfRtcjUrG4hnp\nrFmQR1Kv4ulQm8uB12/u7GHLgXoq64wYzfa+ejhRFEP2ajP2ONhe3kh1g4nvfnaBZLRJhCRcnVpn\nd+g1PxiD2c6PXj7M3JIUNt4wtW8NP/gpLTUXOvyMuFC1ncNtp3Ep7TcmAslgGwOMRiMHD+4jP7+A\nvLwpAcff6605WzNvfAp7YzQq1sybyuq5Jew4cY4/vn+A57ccoOpiO//xiZVBvcDr1q1n586POHBg\nL4cOHWDBgkXjMleJseHs2dMkJiaRnp4x9OABtHf10GI0Y3e6kAkCSoUchUyGR/TQY3PSajJzrqmD\nY+ebaOrtI5gaH8OnV8zihrklKIPI+zvcHuqMNjp6nLhFEb1awZREDXHqwMeRVqXk0U9ezdf+8BZ/\nfP8gv34wM+BBqtfHk5OTS3V1JaIoShL/VzidXXYsdhfT8xMn5PoLp6Xx/sF69p9pkQy2K5yBKY2R\nyKBv3lYdYETZHB52HGlix5EmNCo5IGJzeEiOYDP6xo4av83o0hkZ3H3DVHRqBW6PB48IRyrbMJod\nQedf32rm1Q8quXdt2aV/GRKXJeHWdSg0Kjk6jSLA0DOaHWw/cpHqxq4+R4FGpfBLCw5X2zncdhqX\n0n5jIpAMtjFg69b3cLlcrFlzU8CxZkM35dWNlOakUpA+vjUOMkHgullFzCrI5Cd/38ZHJ84hivDo\n7VcjG7TJFQSBBx/8Cl/5yhd46aU/Mn/+QmkjPEkxmUx0dLSzaNGSiH/D3acv8NrOo1xoMUQ0XqtS\nsmJ6PtfNKmJBSU7ABsIjihypN/LByRbOtllwe/yVzARgdmYs68pSSNb5R50LM5K5bnYR247VcLi6\nkUVTcwOun5s7hYaGeoxGA4mJUu3QlUx92/gLjgykMFNPSryGI1XtOJxuVFGcYiMxfgwlgw6ETC3z\nEamqo93p5q9bKgLqij4+2YxWo2DDqmJvull1e0hjzceRqnbuXOWO6lQxiYkj3LoOhc3hRq0MHcGq\nbzXz6tYq7l0TvC9wsNrO4bbTsDvdlFe0Rjw+GpAMtlHG5XLx9ttvolarWb16bcDxfx06iwjcsnDa\n+E+ul+Q4HT+8dy3f/ev77Dh5jsKMJD61/KqAcQUFRSxffjW7d+/g6NFy5s6dPwGzlbhUGhrqAK9R\nMxSiKPL8lgO8tf80CrmMhSU5TElLRKtS4hFFHC43brcHuVyGVqUkWa9jSmoC+elJQSO1bo/I4cYu\nttUYaO9xApARp6I0RUd6nAqlXEanxcmxpm6ONpk522bhswsyKU72fxjfung6247VsPVoVVCDLSXF\nu+ExGDolg+0Kp18hMm5Cri8IAgunpfHuvjpOnOtgfmnahMxDIvoIJ9ffYbKFTC0Lh29zCf0pXuUV\nrXR2BzfEjlS243C62HmsOejxwZjMjitCgU9i5Ny2soDdx5uGrFMbiKl3PxCKo5Xt3Hld5P1dI2mn\n4VvDbo+Hv26pCHmPRKvqpGSwjTLvv/8ura0trF//SeLi/DcMNqeL98sriddpWD596M3zWKJRKfn2\nXdfz1d+/ycvby1lUmktuSkLAuE9+8g52797Bu+++LRlsk5S2Nq/XKT09fcix/zx4hrf2n2ZKWgKb\n7lxFdvLIGgC7PSLljd1sre6kw+JELhO4uiSZBRkxZOkD1ZxWFSVysKGLN0628eeDF/naijzSYlV9\nx4szU8hK0lNe3YjT5Q6Q+dfpvO0BLBbriOYrcfkwEZL+g1lUls67++o4cKZVMtgk+ggn1z+S1DLo\n31zmEJjiFYyOLhu7IjTWAJL0V4YCn8TIMVuc2IdhrEWCscfet64jIZKUYx+bt1X7RZ+HGh8tRF9V\n3STGZrPy17++iFqt4a67AhtPbztajdnm4Mb5pajGSVo9HPE6Df++bgkut4dXPzoadMy0aTPIzc1j\n//69WK3SZngyYjR2AgwZeWo2dPPH9w+SEKPh+/esGZGx5nR72Fdn4uc7atl8vAWjzcWyKfFsui6f\nzy2fEtRYA29UYlFuPHfPTsfuFnntWAueQQ1g5xZlYXO6ONfcGfB+lcpr3Dmd4dN7JC5/6lvN6NQK\nkkKstfEgLz2W9EQtx2raR30jIzE5sTvdtBos2J3uvpSugSlXvtSy4eLbXNocriFTKn1E1lrby2AF\nPgmJwfiMpaGQDaOqJmmYRlO4+2fgGg6XOhlsfDQhGWyjyEsv/QmDoZPbb/80SUnJfsc8ouhNM5PJ\nuGVR9BTwLps2hcKMJD4+fYH2rp6A44IgsHz51TgcDo4ePTwBM5S4VHp6vL9rTEz4iMNrO4/hcnv4\nwtrFfg2tI6HD4uTdinZ+tO0Cr59oxWhzsTQvnm9dO4XbZ6YRr4nMQTEnK47ZmbHUGW2cafVfj1Oz\nvQ/j6qb2gPfJemvmPB5pc3wlY3e4ae20kJsWO6E1t960yHQcTg/HagLXq8SVg9vj4dWtlTz+/D6+\n9Yd9PP78Pl7dWonbE9jYesOqYq6fn90rLhIZvs2loSt0SlikqJUy4mNUCECyXsPqBTl9aZwSEqFQ\nK+XMikBgadlVGSQMyJwJx0iMpg2rilm9IIdkvQaZEHwNh0udBFg2MyNq1/zEh3kuE06ePMGbb75B\ndnYOd965MeD44eoGGjpMXD+7mMTY4Hmxbo/IyRYzFW0WjFYXaoVAWqyKwiQtRck6FMNxT0SIIAjc\nOL+U372zlx0nzgWtZZs/fyGvvfZXjh4tZ+nSFaM+B4mxxeVyAYRtmN3e1cP249XkpsSzcmZBROft\nsDg51WzmeLOZCwYbAFqFjGsLE1lZkBCxkTaY64uTONZkZm+tiRnp/UZmboo34tfQbgp4j29zLg7H\ndSxx2dHQZkYE8tInpn5tIIumpfHPPRc4cKaVRdOGTkeWuDwZjhKdXCbjnhtKuePaYtqMVtxuDzuP\nN3G8ugNDt61PwMbucJOk95c0T9SrSYxThazLEYShn4/LZ2Xy6WuLJ0VPKonoYvX8HL+m8IPJTNJx\n5oJhSJEb8KpI3rYysn3IQMKlHPsIlzqZrFdz79rSqJT0B8lgGxU6Otr56U+fRBAEvv71b6LRaALG\nvL3/DOAVTwhGndHGq0eaabcMLsTs4UMMaBQypqfHMCM9htKU0S2EXD4tn9+9s5fDNY1BDbapU8tQ\nKBRUVJwZ1etKjC/hIg4fHKnC7RG5benMAMXQgbT1OChv7OZEs5nm3o2BAJQka5mfo2dWZiyqEM2C\n3R6RHqcHtyiikgloFMHHZenVZOvVVLVbsDrdaHsfuBmJ3k14izGwcaYUYZMAqOutX8tLn7j6NR85\nqbFkpcRwvKYDq92FNkjbConLm/DKdW0hlejUSjk5qd41fG+GHvt1/Y19Ab//7jDZiI9Vk6pSUDYl\niT0hanNEERJiVWE3zALBFfgkJIYiSa8hOYQhpFbKaOq0RHwuu9ON2eKMqE+xr+m1Vq3Aanf1GWmh\n1nB4tdbUqHZSSH9BLhGz2cz3v/9tOjra+fznv8SMGYEGT2OHifKaRqbnplGUmRxw/HRLD385fBGP\nCEvz4lmSF09KjBKH20OjyU5lu4XjzWbKG7spb+xGAPKStKTqFCRolKgVMuS9e2xBENAoZaTqlGTH\nq1GG2DwPJD5GQ25KPFWN7XhEMWDDrlKpyMnJpa6uTupzNQmR9/ZCc7uDGzOiKPLRiRrUCjkrZwT3\natUabLxf1UFFm/ehq5AJTEvTMSM9lhnpMUF7qIHXSGu1uWi1uOhqMjNQzT9WISNfryI5SCRuWloM\njV12LhhsTEvzpmfGadWoFXI6ugMf/Eql98HudIZXnpK4vKlv8fYCnEjBkYEsKkvjzd3nOVrVztKZ\nw+uBKDH5CZd+1dFl5+UtFdxxbRFN7T3kpMUSpwueLjZ4A5ocrwlo+Lt8djZ3XV9EeWVbULW+ZL2G\nWUVJbA/TyPhoVQd3XCtJ+EsMn3CG0HD3jAkx6pD1az4DLVan5M1d5zlS2UZHlx2ZAB4RkuJUzCtN\nC9v8OpxaazQjGWyXgMlk4rvf/U+qqiq58cabuf32Twcd98GRKgBuWhhYu9ZgsvFyeRMyQeBzCzOZ\nmtpfO6RWyChLU1CWFsP6aSk0dtk51dJDVbuFBqON2s7w+Q1qucDivHhWFyehGyInviAjifp2E+2m\nHtISAjc76emZXLhwHrPZHKB+KRHd+IwZlyu4MXO+xUBjRxcrZ+QHeLRcHpF/nmnj4wsmRKAwScvi\nXD0zMmJDRsgA7G4PjT1OmnqcuHqXaYJGgUYGCkHA6vLQaXdzstPG9EQNqVr/R9GURG+Uut7Ub7AJ\ngkBinA5DEINNq9UCYLFE7sWTuPyoazUjlwlkpQyvBnOsWDjNa7AdONMiGWxXIEMpP+452dwXEZMJ\nkJUSw7/dNA2lXCB1kCjJQIKlWb616xwWq4MVszJD9nrbsKoYu9MTMgoXrXLmEpOD21YWYLG5OFtr\nwGi2kxinoTQvgb1hFBmDMSdI/ZqvZYXPSaFWyf0cEz5ncGe3Y8jm16FSJ+1ONx0mS9SmA0sG2whp\naKjje9/bxMWLjdxww4089NAjQb0Ibo+HbceqidGoWDZtyqBjIn872oLTI/JvC/yNtcEIgkBOvIac\neA1rpyaTmBxLZW0nJrsLh0vsU9TziCIWp4fmbjvHm8zsPG/keLOZz87PJCc+MFXTR0ZCf7pZMINN\nr9cD0N3dJRlskwy53HubO52uoMf3na0FYNm0fL/X7S4Pf+/Gq7cAACAASURBVD50keoOK2mxKj41\nM42iZG3Ya9lcHurNTposTkRAKRPIi1GQqVOSmxlPW1t331iTw82JDitnjTbiVTq/VMqMOK+nuXVQ\nPUZirJaKhjbcHo+f9yw+3tuSwmiMrNG3xOWHxyPS0GomKyUmaE/AiSAzOYbctFhOnu+kx+YkRjN0\nio/E5cNwmgp7RGho6+EHfzkEgEYlY15JKhvXlCKXCX5pkOEa/n7/c4v6/ntw9EAuk7FhVTGnL3QG\nTY2MVjlzidHFF6UaLcNksDGVpFezdEYGd98wFblMoKLOEHG7ipzUGDauLgl4/dWtVX41ckP1fBuq\n+fXA70AhF3h1a6Xf/OdOTQ0bpZsIJINtBBw4sI+f/eyHWCw9bNhwD/fd90BfDc1gTtY202m2snbe\n1AAp/48vGGkxO7wRi2HWXChkAqmxKlLDKO7cMi2VbdWdfFDVyX/vb+ShZbkhxyfGeTfiBnPwCIVP\nsCJUWp1E9CLv3byKYqAqGcDBqgYUMhnzi7P7XvOIIq8ebaa6w8rM9Bg2zs0IWZsGYHa6qTM7abN6\njUKNXCA3VkWGThGyJi5eJadQr6bKZKehx0nhAFngeI0ChUwIqOlMitPhEUW6LDY/8Z709EwALl4M\nXfQscXnT3GnB4fKQFyXpkD4WTUvjjR3nOFLZzopZmRM9HYlxZsOqYqw2V9i+T8GwOTzsOdXC/jMt\nKBVy7A43iXEq1EpFSGGRji5vhCxY9MBid/G3D85yti608EO0yplLjA7BDCufYXIpBIv4fnyyGa1G\nwcbVU5lVnBJSkMSXyijgbTdhsTnZvK26z1iyWB3811snOXA6uJMiFKGixcG+A51G2de/0zf/oaJ0\nE4FksA2Td955i9/97tcoFAq++c1vc911q8OO33XqAgDXXFXo97rLI7L9nFdM5KayoeVQR4JCJrBm\najLxGgX/c6KVPx26yCMr84LWtcVpvZvlHlvwB7lPadBXDyUxefD05goEiwCbemxUX2xnZn4GOnW/\nMb/rvJFTLT0UJ2u5d14m8iAKpaIo0m5z09jjwOTwGoMxChm5sUpStaENtYFk6BSc67LTanVREKfq\nm6NMEEjUKjBY/aOCKXHeh2+bqcfPYMvOzkGhUFJTUzXkNSUuT2p769fyMqIrA2BhmddgO3i2VTLY\nrkDkMhmfWVvKmdrOkIZWONwecPdGE7zvD3+OrYfquXdtWV/dm6+twO7jF7E5gjvtkgcpTo52BEYi\nOginWPq1u+eP6JzhhXW8Ua5wCpK+VEZfgY8vpdGnp/DxiWas9uDZQeEIFS0O9h2Eiv4NFaUbbySD\nbRhs2fIOzz33DAkJiTzxxI8oLZ0WdrxHFNlfUUe8TsOMPH9Z51MtZrrtblYWJBAzjJ4rI2FxXjxN\n3XZ2XzCxrcbA2qmBwidalTdVx+oIXudkNnu9D7Gx0eW9lhgaXzNpXy3bQI6ev4gIzC3sj66ZbC62\nVHYQo5LzmbkZAcaaRxRptrioNzuwub2P2US1nOwYJUlq+bAKjGWCQJJGQZvVhdUtolP0vzdBo6Ct\nx4rT7elzMqT3KkU2Gbr7+rKBVxhn6tRSzp49jclkIj5++E2/JSY3tc1eg21KFEj6DyQtUceUjDhO\nX+jEbHUSq5XSIq801Eo5s4tTwgp+jBbHazr7mnND4AZ1MAmxKr772QXE6VR9xl20p4ZJDJ+hDCub\nY/hGEYQX1unssnGu0UROWmxIBclQ7DnRPGTaYzjmlCQHGFqRNM0eSLTVdI7oDhSvwGZHp06d4Nln\nf4ler+dnP3tmSGMN4FxTBwazlQUlOQEPu/JG7+ZiUY5+TOY7mBtLU9Cr5WyrMdAZ0DoA1Eqv7W53\nBr9B2tvbUCqVxMWNz3wlRg+bzQqARhNYf3bifBMAswv7Pf9bKjtwuEVuKk0mdpD6o8nu5lCrhSqT\nHbtbJFOnYGGajlnJWpI1ihEpiOqV3nvDPGjtJfRubI0Domy+Xmz1bcaA8yxdugKPx8P27R8Mew4S\nk5+6Fq+CbrQoRA5k0bQ03B6R8mFsFiQuD3xG0PGaDsCbAgYwVvZPR5eNw2db6bY4ItqgdvU4+iIY\nPuOuo8uOSH8EZvO26rGZrMS4Ec6wMnTbMIyw6bpPWCcYggBPv3aUJ188OOy2JpdirEF/xG4gQzXN\nHky01XSGfGScOXOG++67j1tvvZWXXnrJ79j9998/5hOLJlwuF7/61VOIosh3vvMD8vKmDP0moLzG\nGwKeN6A2CMDp9lDZZiEtVkVmiIU+2mgUMm4uS8HtEfnoXKAwg88TYQ8iTCGKIhcvNpCRkSVJ+k9C\nurt90dHAyMPJuha0KgXFve0mumwuDjd0kRqjZGGuv3Fe1+3gaIcVq1skS6dkSbqOqQkadGHUIiNB\n2/t+m8v/EZvYqxzZae13MBRmeOdZ3dQRcJ7Vq9eiVqv5xz9ex+EYfuqRxORFFEVqW8ykJemist/Z\nwtI0AA6eaZngmUiMNwONIOhPAVsxK5PctFiCZJtfMi+8c4av/2Y3T/754JBRDd+mdKgITChnrsTk\nIJxhlRinIXGEe1GfsE4wPCJ9hn9DW8+Izj9SjlV1BKzZcN9BMKKtpjPkTuv73/8+n/3sZ/nBD37A\ngQMH2LRpU9+xKy3C9uGHW2hoqGfduvXMnDkr4vcdqmpAJgjMK/I32C4YbDg9ImWp4xtmnZMVR6JW\nwcH6LnoGeS+UCu+idLgCH8rt7e309PSQl5c3LvOUGF26ukwAAdFRU4+NhnYTZTlpfRHgA/VduEVY\nWZDgV4N2ocvO+W4HapnAnBQtJQnqsCIkw0HT20TQ5vavr0jWeSNsHQMiwvExGjKT4jhd14Lb4z8+\nISGBdevW09rawv/+799HZW4Sk4M2kw2r3cWUKGiYHYyUBC0FmXrO1BrpskjOhCuFcEbQqXMGNt07\nn2ceWsEjG2azcnYGauXohd08IjR1WhjqMe3blA4VgTGZQxt+dqebVoNFMuqimHCG1dypKWhUI3d0\nbVhVzOoFOSTrNQgQ0gkxFs6JUARbs+G+g9y0WJL1GmSCt6Zz9YKcqOvLFvIXUiqVrFq1CoDnnnuO\nRx99lGeeeYavf/3rl3TBH//4xxw7dgxBENi0aROzZkVuAE0U77zzNjKZjLvv/kzE7zFb7ZxtaGNq\ndkqfoIePmg5vilrxEBLpoihidnqw99YJKWQCGrmAWj6yVS+XCawsSOCt0+0cqDdxXVFS37H+lMjA\nCNu5c950iIKCohFdV2JiMZm86YOD67rONnhVl6b31leKosihxi6UMoF5Wf3RuHari1qzE41cYHay\nNmz/tZGg7t1R+Na5D5+iaesgRbM5BVm8e7iCM/WtzJzi39tq48b72LFjG3/964vMmTOPsrLpozpX\niegkWuvXBrJoWhrnm7oor2zj2jnZQ79BYtITiRGUlqhjZkEyMwuSueOaYr7zwj66LMHriZLiVDhc\nHszWyOuN3MF1RtCo5KyYldm3KQ3XM25gathgOfRgqoNfvXNuxPOTGD/GqmH0wL5m5xpNPP3a0aDj\nPKMU65HLvP8LV3YXKp0x3HfgcotRLbYT1qTev38/ixcvBuBnP/sZX/3qV/n5z3+O0xlcmGIoDhw4\nQG1tLZs3b6ampoZNmzaxefPmEZ1rvGhra6WqqoJ58xaQkhLcMg/G0XMX8Ygi84tzAo6dN3gNtvyk\n4Aab2CvqcL7bgTPICpcBsQY7KkR0Chk6hYxYpRydQhgyZXFBjp53znZQ3tjtZ7BpIjDYiooCe2NI\nRD9Go5G4OH2AwmdlYztAn3hHU7eD9h4nszNj0fQ+rFwekQqjDZkAM5M0o26sAcgFr+dtsMGWHqtC\nAC52+Rtsi0vzePdwBXvP1AYYbHFxcXzzm99m06bHePLJ7/D008+SlSVtji93LjR1AZCfGb01tgtK\n09i8rZqDZ1olg+0KIVIjyEecTsWi6RlBRUKWzcxgw6pinnzx4LAMNoB5JSnUtpgxdNtIiFVTNiWR\njTeUoFP3C+CE6xk3d2pK0F5VoeTQdVoVty3PH9YcJcaeUA2jRwu1Uk5hdnzINZ+sV2O2OrE7Q3gR\nIsTtgayUWL+1N5hQ6YzhvgO5jKgRGAlGyN3X448/zlNPPdWnDqhQKPjd736HVquloqJiRBfbu3cv\nq1d7ZfCLioowmUx9549WTp48DsD8+QuH9b5Q9Wtuj0id0UZ6rApdiBvlXJeDSpMdt+gVdSjSqyjS\nq7xy6RoFOqUMi8NNu83b++qs0c6hNgt7mns4Y7BhsLtCpq3qlHJKU3Q0dTv8Ihc+lchgSkG1tRcA\nKCgoDDgmEf10dQVXTTzX0gnQV79W0ebtwTcjvb+Be73ZgUuEKbEqYsbI4yQI3sixfZArWK2QkRar\nosFk62sMD16BlFiNil2nzgekRQLMmTOPBx/8KgZDJ5s2PUZLy/D6H0lMPi5MgghbcryGoiw9Z+sM\ndPVIaZFXAkOloQXbUA5MLxuYnvVvN5VhtbuGJZoAXmfY3atL+OEXFvPjLy7hR19cwudvme5nrA11\n7Q2rioMKkoTaMO872SSlR0YxvpYPYxFFCrfmy/IScVyisebDYnNy3dwskuK8Tg9fumWyXh1ROuNY\nfgdjRcgIW2lpKa+//rrfazKZjIceeoiHHnoIgOeff54vfOELEV+svb2dGTNm9P07KSmJtra2qJaK\n9xkrxcWRN8/ziCIHqxrQ69R9m2EfTd12nG6R/ERN0Pe2WV009DjRKQRmJWv70sUGk5ISS2NLFxaX\nhx6nB7PTg9HhptXqotXqIk4pozRBQ0yQnPirMmI53drD6ZYe0nrTzjS9BpsliKx/fX0tarWG1NS0\niL8DiehAFEXM5m4yM7MCjtW1GkiI0RAf412L5zq9BltxstfD5PaIXOxxopRBdszYSpFr5DIsLjcu\nj4hiQKL7lEQNLWYHF7vs5MR756mUy1k5o4B3D1dQXt3Iwqm5Aef7xCdup6fHzMsv/5nHHnuYn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Ln7yqz3Hg9ngQAY1Khi1MA22ZTAYM/yZQKeWolHIstsB691AkxqkDBHYGEkz1MlivwvEg5N24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+0kkpGRhcFgYM+e3QHiCp0Zk5vGNzsPs+lgaciXN4BaIWN+Xjzz8+KxOj1Umh2YbG5anR4c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SlfLMHTbMHp8EwPo9w3OV7cx95wVBEARBEARBOGmd4ZD5GdFhbsngKsiOxahTsWFfLT6/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\nEREREQmKDRsREREREZGg/gvT8y5wjsdAlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f9008d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a Pandas DataFrame of posterior samples.\n",
    "samples_df = pd.DataFrame(data = samples, index=range(n_samples))\n",
    "# Now create a small subset by taking the first 5 weights, labelled as W_0, ... , W_4.\n",
    "samples_5 = pd.DataFrame(data = samples_df[list(range(5))].values,columns=[\"W_0\", \"W_1\", \"W_2\", \"W_3\", \"W_4\"])\n",
    "# We use Seaborn PairGrid to make a triale plot to show auto and cross correlations.\n",
    "g = sns.PairGrid(samples_5, diag_sharey=False)\n",
    "g.map_lower(sns.kdeplot, n_levels = 4,cmap=\"Blues_d\")\n",
    "g.map_upper(plt.scatter)\n",
    "g.map_diag(sns.kdeplot,legend=False)\n",
    "plt.subplots_adjust(top=0.95)\n",
    "g.fig.suptitle('Joint posterior distribution of the first 5 weights')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load up an image from the test data and see what classification we get for each sample from the posterior distribution of weights and biases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "truth =  7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5f7b91da90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wQWfPnlVbW5uys7MlSa+88oqWLl0ayzoBIKHChuWFCxd05coV+f1+dXd3q7S0VPPnz9f2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f90141e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the first image from the test data and its label.\n",
    "test_image = X_test[0:1]\n",
    "test_label = Y_test[0]\n",
    "print('truth = ',test_label)\n",
    "pixels = test_image.reshape((28, 28))\n",
    "plt.imshow(pixels,cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Now the check what the model perdicts for each (w,b) sample from the posterior. This may take a few seconds...\n",
    "sing_img_probs = []\n",
    "for w_samp,b_samp in zip(w_samples,b_samples):\n",
    "    prob = tf.nn.softmax(tf.matmul( X_test[0:1],w_samp ) + b_samp)\n",
    "    sing_img_probs.append(prob.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5f7946ce10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+ZcoU9ezZU1FRUXr++ef1xhtv6De/+Y1r6NOnn37atR1PmzZN+/fv1+TJk7Vw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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f90061630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of these predictions.\n",
    "plt.hist(np.argmax(sing_img_probs,axis=2),bins=range(10))\n",
    "plt.xticks(np.arange(0,10))\n",
    "plt.xlim(0,10)\n",
    "plt.xlabel(\"Accuracy of the prediction of the test digit\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, our model is very confident about the prediction here. For all the samples from the posterior, we predict the true value (this figure may be slightly different in different machines due to the ranom number generation)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How does the model handle unfamiliar data?\n",
    "\n",
    "What will the model tell us if we give it **completely different data** to that which we have used for training? This is a crucial test as we need to know not only a prediction but also the confidence with which we can claim that prediction. In many Artificial Intelligence applications this information extremely valuable. \n",
    "\n",
    "For this test we shall use the *notMNIST* data of alphabets and see how our model reacts to this data. Our hope is that if we pass it an unfamiliar image, say of a letter 'D', when it has previously only been trained on the digits {0,1,...,9}, then the model should output a classification but be highly uncertain about it.\n",
    "\n",
    "We can download the notMNIST images from [here](http://yaroslavvb.com/upload/notMNIST/). The conversion to MNIST format is described [here](https://github.com/davidflanagan/notMNIST-to-MNIST). In this example we just use the converted data. We assume that all the required files are in the `notMNIST` folder and use the TensorFlow methods.\n",
    "\n",
    "```bash\n",
    "git clone git@github.com:davidflanagan/notMNIST-to-MNIST.git\n",
    "mkdir notMNIST_data\n",
    "cp notMNIST-to-MNIST/*.gz notMNIST_data\n",
    "```\n",
    "\n",
    "We assume that the `notMNIST_data` is in the same directory as this notebook. Since the images are the same format as MNIST, we can use TensorFlow to load the images as before.\n",
    "\n",
    "<div class=\"alert alert-danger\">\n",
    "Note that if we haven't executed the bash commands above, TensorFlow method will download the MNIST data into notMNIST_data directory below and we will get the digit 7 as the first image, instead of the alphabet D from the notMNIST data. Please make sure that we have the correct data in notMNIST_data directory before executing the cell below.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./notMNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting ./notMNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting ./notMNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting ./notMNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# As the nonMNIST data is in the same format as MNIST, we can use the TensorFlow functions.\n",
    "# Please make sure that notMNIST_data directory is in the same directory as this notebook.\n",
    "# Otherwise, please provide the full path.\n",
    "\n",
    "### Note that if you haven't executed the bash commands above, TensorFlow method\n",
    "### WILL download the MNIST data into notMNIST data below and you will get the \n",
    "### digit 7 as the first number!\n",
    "not_mnist = input_data.read_data_sets(\"./notMNIST_data/\", one_hot=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the test images from the data and their lables. \n",
    "Xnm_test = not_mnist.test.images\n",
    "# Once again convert one-hot-vector to the corresponding labels.\n",
    "Ynm_test = np.argmax(not_mnist.test.labels,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now load an image from the notMNIST dataset and ask the model to classify it. As always, we use a set of samples from the posterior to perform the classifications and to see how confident our model is about the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "truth =  3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5f7939a2b0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f8803b7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the first image from the notMNIST.\n",
    "test_image = Xnm_test[0]\n",
    "test_label = Ynm_test[0]\n",
    "print('truth = ',test_label)\n",
    "pixels = test_image.reshape((28, 28))\n",
    "plt.imshow(pixels,cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# As before, compute the proabilities for each class for all (w,b) samples from the posterior.\n",
    "nm_sing_img_probs = []\n",
    "for w_samp,b_samp in zip(w_samples,b_samples):\n",
    "    prob = tf.nn.softmax(tf.matmul( Xnm_test[0:1],w_samp ) + b_samp)\n",
    "    nm_sing_img_probs.append(prob.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f5f78fa14a8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QIEnavHmzIiIinBoeAIBqx7Ej7bfeekuZmZm6//77fcvmzp2rhx9+WGvWrFHjxo3Vv39/\np4YHAKDacay0Bw0apEGDBpVavnLlSqeGBACgWmNGNAAALEFpAwBgCUobAABLUNoAAFiC0gYAwBKU\nNgAAlqC0AQCwBKUNAIAlKG0AACxBaQMAYAlKGwAAS1DaAABYgtIGAMASlDYAAJagtAEAsASlDQCA\nJShtAAAsQWkDAGAJShsAAEtQ2gAAWILSBgDAEpQ2AACWoLQBALAEpQ0AgCVcgQ6AwPvzQ+sDHQEA\n4AeOtAEAsASlDQCAJShtAAAsQWkDAGAJShsAAEtQ2gAAWILSBgDAEpQ2AACWoLQBALAEpQ0AgCUo\nbQAALEFpAwBgCUobAABLUNoAAFiC0gYAwBKUNgAAlqC0AQCwBKUNAIAlKG0AACxBaQMAYAlKGwAA\nS1DaAABYgtIGAMASlDYAAJZwtLS/+uor3XjjjXrppZckSUeOHFF0dLQ8Ho/Gjh2rvLw8J4cHAKBa\ncay0s7OzNWvWLF133XW+ZYsXL5bH49Hq1avVrFkzxcXFOTU8AADVjmOlHRoaqhUrVqhBgwa+ZV6v\nVz179pQkRUZGKjEx0anhAQCodlyOrdjlkstVcvU5OTkKDQ2VJIWHhystLc2p4QEAqHYcK+2KGGP8\nup/bHeZwkl+fjZnhPJ4XzrNxH5PZebblLU+llnatWrWUm5urmjVrKjU1tcSp83NJSztZCcl+PW53\nmHWZUTl4XjjPtn1s4+8L2zLbllcq/4+MSv3IV9euXZWQkCBJ2rx5syIiIipzeAAArObYkfaePXsU\nGxurQ4cOyeVyKSEhQY899pgmTpyoNWvWqHHjxurfv79TwwMAUO04VtodOnTQqlWrSi1fuXKlU0MC\nAFCtMSMaAACWoLQBALAEpQ0AgCUobQAALEFpAwBgCUobAABLUNoAAFiC0gYAwBKUNgAAlqC0AQCw\nBKUNAIAlKG0AACxBaQMAYAlKGwAAS1DaAABYgtIGAMASlDYAAJagtAEAsASlDQCAJShtAAAsQWkD\nAGAJShsAAEtQ2gAAWILSBgDAEpQ2AACWoLQBALAEpQ0AgCUobQAALEFpAwBgCUobAABLUNoAAFiC\n0gYAwBKUNgAAlqC0AQCwBKUNAIAlKG0AACxBaQMAYAlKGwAAS1DaAABYgtIGAMASlDYAAJagtAEA\nsASlDQCAJShtAAAsQWkDAGAJShsAAEtQ2gAAWILSBgDAEpQ2AACWcFX2gLNnz1ZSUpKCgoI0efJk\nXXXVVZUdAQAAK1VqaX/yySc6ePCg1qxZo2+++UaTJ0/WmjVrKjMCAADWqtTT44mJibrxxhslSS1a\ntNDx48d16tSpyowAAIC1KrW0jx07pvr16/tuX3zxxUpLS6vMCAAAWKvSX9MuzhhT4X3c7rBKSPLr\nsi3zxsf7BTrCbwLPC+fZto8lMlcG2/KWp1KPtBs0aKBjx475bh89elRut7syIwAAYK1KLe1u3bop\nISFBkrR37141aNBAderUqcwIAABYq1JPj3fq1Ent27dXVFSUgoKCNH369MocHgAAqwUZf15YBgAA\nAceMaAAAWILSBgDAEgH9yFd5bJzu9KuvvlJMTIzuuusuDRkyJNBxKjRv3jzt3LlTBQUFuueee9Sr\nV69ARypXTk6OJk6cqPT0dP3000+KiYlRZGRkoGNVKDc3V3379lVMTIwGDBgQ6Djl8nq9Gjt2rFq1\naiVJat26taZOnRrgVBXbsGGDnn32WblcLt13333q3r17oCOV67XXXtOGDRt8t/fs2aPPPvssgInK\nd/r0aU2YMEHHjx9Xfn6+Ro0apYiIiEDHKldRUZGmT5+uAwcOKCQkRDNmzFCLFi0CHesXq5KlbeN0\np9nZ2Zo1a5auu+66QEfxy/bt23XgwAGtWbNGmZmZuvXWW6t8aW/ZskUdOnTQiBEjdOjQIQ0fPtyK\n0n766adVt27dQMfwW5cuXbR48eJAx/BbZmamnnzySa1du1bZ2dlasmRJlS/tgQMHauDAgZLO/L57\n++23A5yofK+//rp+//vf66GHHlJqaqruvPNObdq0KdCxyvXee+/p5MmTevXVV/XDDz/o0Ucf1fLl\nywMd6xerkqV9rulOq/LHw0JDQ7VixQqtWLEi0FH80rlzZ9/Zi4suukg5OTkqLCxUcHBwgJOdW58+\nfXxfHzlyRA0bNgxgGv988803+vrrr6t8idgsMTFR1113nerUqaM6depo1qxZgY50Xp588kk99thj\ngY5Rrvr16+vLL7+UJJ04caLEzJZV1ffff+/7Hde0aVMdPny4yv+O80eVfE3bxulOXS6XatasGegY\nfgsODlatWrUkSXFxcfrjH/9ozZM5KipK48aN0+TJkwMdpUKxsbGaOHFioGOcl6+//lojR47UX/7y\nF3388ceBjlOhH3/8Ubm5uRo5cqQ8Ho8SExMDHclvu3fvVqNGjar8JFO33HKLDh8+rJtuuklDhgzR\nhAkTAh2pQq1bt9ZHH32kwsJCffvtt0pOTlZmZmagY/1iVfJI++f4VJpz3n33XcXFxen5558PdBS/\nvfrqq9q3b5/+/ve/a8OGDQoKCgp0pDKtW7dOHTt21GWXXRboKH5r3ry5Ro8erd69eys5OVlDhw7V\n5s2bFRoaGuho5crKytLSpUt1+PBhDR06VFu2bKmyz4vi4uLidOuttwY6RoXWr1+vxo0b67nnntP+\n/fs1efJkxcfHBzpWuW644Qbt2rVLgwcP1hVXXKHLL7+8WnRJlSxtpjutHB9++KGWLVumZ599VmFh\nVX9u3j179ig8PFyNGjVS27ZtVVhYqIyMDIWHhwc6Wpm2bt2q5ORkbd26VSkpKQoNDdWll16qrl27\nBjraOTVs2ND3MkTTpk11ySWXKDU1tUr/4REeHq6rr75aLpdLTZs2Ve3atav086I4r9erhx9+ONAx\nKrRr1y5df/31kqQ2bdro6NGjVpxqfuCBB3xf33jjjVY8JypSJU+PM92p806ePKl58+Zp+fLlqlev\nXqDj+GXHjh2+MwLHjh1TdnZ2lX5t7YknntDatWv1r3/9SwMHDlRMTEyVLmzpzLuwn3vuOUlSWlqa\n0tPTq/x7B66//npt375dRUVFyszMrPLPi7NSU1NVu3btKn8WQ5KaNWumpKQkSdKhQ4dUu3btKl/Y\n+/fv16RJkyRJH3zwgdq1a6caNapk5Z2XKnmkbeN0p3v27FFsbKwOHTokl8ulhIQELVmypMoW4ltv\nvaXMzEzdf//9vmWxsbFq3LhxAFOVLyoqSlOmTJHH41Fubq6mTZtWLf4TViU9evTQuHHj9N577yk/\nP18zZsyo8qXSsGFD3XzzzbrjjjskSQ8//LAVz4u0tDRdfPHFgY7hl0GDBmny5MkaMmSICgoKNGPG\njEBHqlDr1q1ljNHtt9+uCy64oMq/2c9fTGMKAIAlqv6fowAAQBKlDQCANShtAAAsQWkDAGAJShsA\nAEtQ2qgSjh49qnbt2umZZ54JdJRfza5du9SzZ0899dRTJZanpqb6ptpcsmSJFi5cGIh4pYwbN07x\n8fFKS0vTfffdV+59N27cqKKiIklSdHS0CgsLHcsVGxurvn376vPPPy+xfNu2bcrKypJ05qNqBw8e\n/J/WX/zncb527dql5OTkUsujo6P173//u9zHFt+HxbcFKA+ljSph3bp1atGiRZWfGvF8JCYm6k9/\n+pNiYmJKLPd6vdq+fXuAUlXM7XZXeJWvJUuW+Apn1apVjk608c4772jRokW68sorSyz/5z//qePH\nj//i9f+Sn0d8fHyZpe2P4vvw19oWVH9VcnIV/PasXbtWM2bM0MSJE7Vr1y516tRJkpSUlKTZs2cr\nJCREdevWVWxsrGrVqqVHHnlEe/bskSQNGzZMvXv3Vo8ePbRy5Uo1a9ZMXq9XTzzxhF555RVFR0er\nTZs22rdvn1544QWtWbNG69evV0hIiC644AItXLhQF110Uamx5s6dq379+umFF17wTePZp08fLV68\nWC1btvRlT0pK0ty5c+VyuRQUFKRp06YpKytLa9eulTFGF154oUaPHi1JSk5O1hNPPCFjjG/indTU\nVN1333369ttv1aVLF02bNk2StGDBAu3atUu5ubnq3Lmzxo8fX2I+7bPb2LhxYx06dEhhYWFauHCh\nsrKydO+996p169Zq1aqVRo4cWea6jDGaMmWKvvzySzVp0kTZ2dmSzlyAw+Px6IMPPlB6eromTZqk\nkydPKjg4WNOmTdOmTZt08OBB3XXXXVq6dKmuvfZa7d27V3l5eZo6dapSUlJUUFCgfv36yePxKD4+\nXv/+979VVFSk7777Tk2aNNGSJUtKzQ3+1FNPaevWrXK5XGrVqpUefvhhLV26VKmpqZo4caKmTp3q\nu2rT6tWrtWPHDo0bN05z5syRJL3xxhvauXOnDh06pOnTp6tr1646fPiw/vGPfygnJ0fZ2dl68MEH\nS8xK9/Ofx+DBgzVz5kwdPHhQp0+fVt++fTV8+HB99dVXmjZtmkJCQpSbm6tRo0YpPz9fmzZt0u7d\nuzVp0qRzXpZ31apVevvtt1VYWKjLL79c06dP1zPPPOPbh926dSuxLQUFBYqNjVVBQYHy8/M1bdo0\ntWvXrtTzuKrPSAaHGCDAPvnkE9OjRw9TVFRkFixYYKZMmeL73k033WS+/PJLY4wxK1euNG+88YZ5\n/fXXzZgxY4wxxhw/ftyMGDHCFBQUmMjISPP9998bY4zZvn27iYqKMsYYM2TIELNgwQLfOp9//nlz\n8uRJY4wxU6dONatWrTrnWEuWLDGLFy82xhizf/9+M2jQoFL5e/XqZZKSkowxxrz//vtmyJAhxhhj\nFi9eXGLcs4ovX7x4sYmKijL5+fkmNzfXdOzY0WRkZJi33nrLjB8/3veYmJgY895775VYz/bt282V\nV15pUlJSjDHGjBs3zrzwwgsmOTnZtG3b1nzzzTfGGHPOdX344YfmjjvuMEVFRSY7O9t069bNrF27\n1iQnJ5uIiAhjjDGTJk0yL730kjHGGK/Xa+bNm2eMMaZ169YmPz+/xNfLli0zM2bMMMYYk5OTYyIj\nI80PP/xg1q5da3r06GFycnJMUVGR6dmzp9m7d2+Jbdm1a5fp16+fycvLM8YYM2bMGBMfH2+MMSV+\nrsUVXx4ZGWlWr15tjDFm3bp15p577jHGGDNixAiTmJhojDHm6NGjJjIy0pe7rJ/HihUrzKJFi4wx\nxhQUFJgBAwaYffv2mVmzZpnly5cbY4w5duyYef31140xZ55bH3/8calsZ5cnJSWZ6OhoU1RUZIwx\n5tFHHzUvvvhiqX1YfFv69u1rDh48aIwxZt++febWW2/1rbOs5xN+WzjSRsCdvdJRUFCQBgwYoAED\nBmjKlCnKycnRiRMn1Lp1a0nSXXfdJUmaOXOmrr32WklnrgXuz+vgZ4/cJalevXq6++67VaNGDR06\ndEhut1sZGRlljpWamqqhQ4dq9OjRevvtt3XbbbeVWO+JEyeUnp7uOwLs0qWLHnzwwfPa/muuuUYu\nl0sul0v169fXyZMn5fV69Z///EfR0dGSzswV/+OPP5Z6bMuWLX1zg3fq1En79u1Tjx49VLduXV1+\n+eWSdM51FRQU6Oqrr1ZQUJAuvPBC3zYUt3v3bg0bNsy3bV26dDnndiQlJWnAgAGSpJo1a6pDhw7a\nu3evJOmqq67yXbq2UaNGpU4FJyUlqXPnzgoJCfGN9fnnn5/XFbDOZrv00kt14sQJ37afPn1aTz75\npKQzl9Atbz51r9erlJQUffrpp5KkvLw8/fDDD7r55ps1ceJEHT58WJGRkerXr59fmbxer3744QcN\nHTpUkpSdnS2X69y/dtPT0/Xdd99pypQpvmWnTp3ynUYv/jzGbxOljYA6deqUNm/erEaNGumdd96R\nJBUVFSkhIUE33HBDmZfSCwoK8v0SO5f8/PwSt8+WQUpKimJjY/Xmm28qPDxcsbGxvnWWNVbDhg3V\nokUL7dy5Ux988IFWrVpVKktxZa2jIj8/zWmMUWhoqO644w799a9/LfexxcczxvjynN1eSedc13PP\nPVcif1n71J99Xfy+P892dllZ2+jvY/1VvAzPrj80NFRLlizxe47v0NBQjRo1Sn/6059Kfe+NN95Q\nYmKi4uO0ZfTUAAADqklEQVTjtWHDBj3++ON+ra9Hjx6+lzz8uX9ISEip59lZxX+u+G3ijWgIqDfe\neEOdO3fWW2+9pfXr12v9+vWaOXOm4uPjVb9+fdWrV0+7d++WdKZkXn75ZV199dX68MMPJZ05ahw4\ncKDy8vJUp04dHTlyRJLO+cai9PR01a9fX+Hh4crKytJHH32kvLy8c44lnblYwuOPP662bduqdu3a\nJdYXFhYmt9vtuwJSYmKiOnbsWO42BwUFqaCgoNz7XHPNNXrnnXd891u6dKm+//77Uvf79ttvdfTo\nUUnSzp07dcUVV/i9rpYtWyopKUnGGJ06dcq3DcUV39c7duzQhAkTzrkNf/jDH3z3zc7O1t69e9W+\nfftyt/Osjh07yuv1+v7YSkxM1B/+8IdyH+Pvfnz77bclSRkZGXr00UfLXU/x+xcVFWnOnDnKysrS\nqlWrlJKSoh49eujRRx/17augoKBSfyAW16lTJ33wwQc6ffq0JOnll1/WZ599Vmrcs1+HhYXpd7/7\nnbZt2yZJ+u6777R06dJytxG/LRxpI6Di4uI0atSoEstuvvlmzZ07Vz/++KPmz5+v2bNny+VyKSws\nTPPnz9eFF16oXbt2KSoqSgUFBRo+fLhCQ0M1fPhwTZkyRc2bNz/nacS2bduqWbNmuv3229W0aVPd\nd999mjFjhm644YYyx5KkiIgITZ482VdYPxcbG6u5c+cqODhYNWrUqPAKSP/3f/+nBx54QCEhIed8\nM1GvXr30n//8R1FRUQoODla7du3KvKZ1y5YttWDBAh08eFB169ZV//79lZGR4de6LrvsMm3YsEED\nBw5U48aNy/xjY+zYsZo0aZK2bNkiY4zviDEiIkK33Xabnn76ad99o6OjNXXqVA0ePFh5eXmKiYnR\n7373O33yySfl7g/pTOHfcsstGjx4sGrUqKH27durb9++5T7m+uuv18iRI31nS8oyZcoUTZs2TW++\n+aby8vJ07733lrpP8Z/HvffeqwMHDmjQoEEqLCxU9+7dVa9ePV1++eV66KGHVLt2bRUVFemhhx6S\ndOYywtOnT9fkyZPVq1evUuu+8sorNXjwYEVHR+uCCy5QgwYNfC8hFN+HxbclNjZWjzzyiJ555hkV\nFBRo4sSJFe4//HZwlS+gArt379acOXP0yiuvBDpKCcXfIQ/gt4EjbaAcM2fOVFJSku+oGwACiSNt\nAAAswRvRAACwBKUNAIAlKG0AACxBaQMAYAlKGwAAS1DaAABY4v8BkahijU4Z0X4AAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5f90214d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now compute the histogram of perdictions from the (w,b) samples.\n",
    "# In our previous test, all the weights from the posterior was able get the correct prediction.\n",
    "# However, here we see that the model gives a wide range of possibilites.\n",
    "# Hence we conclude that its cofidence is lower. \n",
    "plt.hist(np.argmax(nm_sing_img_probs,axis=2),bins=range(10))\n",
    "plt.xticks(np.arange(0,10))\n",
    "plt.xlim(0,10)\n",
    "plt.xlabel(\"Accuracy of the prediction of the test letter\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This histogram shows that the model is not as confident as before about the new classification. This is where the Bayesian methods can add a lot of value compared to the traditional ML methods. What we have shown here is that the posterior distribution of weights can be used to check the confidence with which the network can classify objects. \n",
    "\n",
    "## Summary\n",
    "\n",
    "We have learned to construct a simple Bayesian statistical model for MNIST image classification using TensorFlow and Edward. Understanding uncertainty in statistical inference is very important for a variety of applications and we have explored some basic methods for visualising this problem.\n",
    "\n",
    "---\n",
    "\n",
    "*To finish off we watermark our environment. You will need [watermark](https://github.com/rasbt/watermark) iPython [extension](https://ipython.org/ipython-doc/3/config/extensions/index.html) to execute the following.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.6.1\n",
      "IPython 6.0.0\n",
      "\n",
      "numpy 1.12.1\n",
      "pandas 0.20.1\n",
      "edward 1.3.1\n",
      "tensorflow 1.1.0\n",
      "seaborn 0.7.1\n",
      "matplotlib 2.0.2\n",
      "\n",
      "compiler   : GCC 4.4.7 20120313 (Red Hat 4.4.7-1)\n",
      "system     : Linux\n",
      "release    : 4.8.0-53-generic\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -m -p numpy,pandas,edward,tensorflow,seaborn,matplotlib"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
